{"id":3513,"date":"2025-09-01T11:12:58","date_gmt":"2025-09-01T09:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/pancreatic-cancer\/"},"modified":"2026-02-11T12:01:56","modified_gmt":"2026-02-11T11:01:56","slug":"pancreatic-cancer","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/pancreatic-cancer\/","title":{"rendered":"Pancreatic cancer prediction with machine learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3513\" class=\"elementor elementor-3513\" data-elementor-post-type=\"learning\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4913e6aa elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4913e6aa\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-28db089\" data-id=\"28db089\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-67cca9f elementor-widget elementor-widget-heading\" data-id=\"67cca9f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Introduction<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c58495b elementor-widget elementor-widget-text-editor\" data-id=\"c58495b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\" data-start=\"171\" data-end=\"330\">Machine learning is helping improve early detection of pancreatic ductal adenocarcinoma (PDAC), a cancer with low survival rates when diagnosed late.<\/p><p style=\"text-align: justify;\" data-start=\"332\" data-end=\"496\">We implemented a neural network model using urinary and blood biomarkers\u2014creatinine, LYVE1, REG1B, TFF1, and plasma CA19-9\u2014across 590 patient samples.<\/p><p style=\"text-align: justify;\" data-start=\"498\" data-end=\"689\">The model achieved high AUC and accuracy, showing potential as a non-invasive diagnostic support tool to distinguish healthy individuals, benign hepatobiliary disease, and PDAC.<\/p><p style=\"text-align: justify;\" data-start=\"691\" data-end=\"785\">Healthcare professionals can test this methodology by downloading Neural Designer.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-010bccb e-flex e-con-boxed e-con e-parent\" data-id=\"010bccb\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5eff98b elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"5eff98b\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/www.neuraldesigner.com\/downloads\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-download\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M216 0h80c13.3 0 24 10.7 24 24v168h87.7c17.8 0 26.7 21.5 14.1 34.1L269.7 378.3c-7.5 7.5-19.8 7.5-27.3 0L90.1 226.1c-12.6-12.6-3.7-34.1 14.1-34.1H192V24c0-13.3 10.7-24 24-24zm296 376v112c0 13.3-10.7 24-24 24H24c-13.3 0-24-10.7-24-24V376c0-13.3 10.7-24 24-24h146.7l49 49c20.1 20.1 52.5 20.1 72.6 0l49-49H488c13.3 0 24 10.7 24 24zm-124 88c0-11-9-20-20-20s-20 9-20 20 9 20 20 20 20-9 20-20zm64 0c0-11-9-20-20-20s-20 9-20 20 9 20 20 20 20-9 20-20z\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6c9cca4 elementor-widget elementor-widget-heading\" data-id=\"6c9cca4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Contents<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4efc6c2 elementor-widget elementor-widget-text-editor\" data-id=\"4efc6c2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The following index outlines the steps for performing the analysis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9c4e544 e-grid e-con-boxed e-con e-parent\" data-id=\"9c4e544\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f5434c3 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"f5434c3\" data-element_type=\"widget\" id=\"model_type\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#model_type\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">1.Model type<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-987425d elementor-align-center elementor-widget elementor-widget-button\" data-id=\"987425d\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#dataset\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">2.Dataset<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e5ca1ba elementor-align-center elementor-widget elementor-widget-button\" data-id=\"e5ca1ba\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#neural_network\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">3.Neural network<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-40d3bf8 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"40d3bf8\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#training_strategy\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">4.Training strategy<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-adb443c elementor-align-center elementor-widget elementor-widget-button\" data-id=\"adb443c\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#testing_analysis\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">5.Testing analysis<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-826322c elementor-align-center elementor-widget elementor-widget-button\" data-id=\"826322c\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#model_deployment\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">6.Model deployment<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c4d1b4 elementor-widget elementor-widget-heading\" data-id=\"5c4d1b4\" data-element_type=\"widget\" id=\"model_type\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">1. Model type<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-567d661 elementor-widget elementor-widget-text-editor\" data-id=\"567d661\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li style=\"text-align: justify;\"><strong data-start=\"57\" data-end=\"74\">Problem type:<\/strong> Multiclass <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#Classification\">classification<\/a> (no pancreatic disease, benign hepatobiliary disease, or pancreatic cancer)<\/li>\n \t<li style=\"text-align: justify;\"><strong data-start=\"179\" data-end=\"188\">Goal:<\/strong> Model the probability of pancreatic cancer versus non-cancerous or healthy conditions based on clinical, biochemical, and diagnostic variables to support early detection and clinical decision-making using artificial intelligence and machine learning.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-91b925a elementor-widget elementor-widget-heading\" data-id=\"91b925a\" data-element_type=\"widget\" id=\"dataset\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">2. Data set<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a371d8b elementor-widget elementor-widget-heading\" data-id=\"a371d8b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Data source<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c0e9c3 elementor-widget elementor-widget-text-editor\" data-id=\"5c0e9c3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The dataset pancreatic-cancer.csv includes 509 rows (instances) and 14 columns (variables) for study samples and cancer risk factors.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a0820ba e-flex e-con-boxed e-con e-parent\" data-id=\"a0820ba\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b01b86a elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"b01b86a\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/pancreatic-cancer.csv\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-file-download\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224 136V0H24C10.7 0 0 10.7 0 24v464c0 13.3 10.7 24 24 24h336c13.3 0 24-10.7 24-24V160H248c-13.2 0-24-10.8-24-24zm76.45 211.36l-96.42 95.7c-6.65 6.61-17.39 6.61-24.04 0l-96.42-95.7C73.42 337.29 80.54 320 94.82 320H160v-80c0-8.84 7.16-16 16-16h32c8.84 0 16 7.16 16 16v80h65.18c14.28 0 21.4 17.29 11.27 27.36zM377 105L279.1 7c-4.5-4.5-10.6-7-17-7H256v128h128v-6.1c0-6.3-2.5-12.4-7-16.9z\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Dataset<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1d9ac51 elementor-widget elementor-widget-heading\" data-id=\"1d9ac51\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Variables<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7cadb4b elementor-widget elementor-widget-text-editor\" data-id=\"7cadb4b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The following list summarizes the variables&#8217; information:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d80a0e0 elementor-widget elementor-widget-heading\" data-id=\"d80a0e0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Demographic information<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a37c760 elementor-widget elementor-widget-text-editor\" data-id=\"a37c760\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul data-start=\"461\" data-end=\"553\">\n \t<li data-start=\"461\" data-end=\"502\" style=\"text-align: justify;\">\n<p data-start=\"463\" data-end=\"502\"><strong data-start=\"463\" data-end=\"478\">age (years)<\/strong> \u2013 Age of the patient.<\/p>\n<\/li>\n \t<li data-start=\"503\" data-end=\"553\" style=\"text-align: justify;\">\n<p data-start=\"505\" data-end=\"553\"><strong data-start=\"505\" data-end=\"518\">sex (M\/F)<\/strong> \u2013 Biological sex of the patient.<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-74b4706 elementor-widget elementor-widget-heading\" data-id=\"74b4706\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Biomarkers<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8509c7c elementor-widget elementor-widget-text-editor\" data-id=\"8509c7c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul data-start=\"572\" data-end=\"1004\">\n \t<li data-start=\"572\" data-end=\"673\" style=\"text-align: justify;\">\n<p data-start=\"574\" data-end=\"673\"><strong data-start=\"574\" data-end=\"591\">plasma_CA19_9<\/strong> \u2013 Blood plasma levels of CA 19-9 antibody, often elevated in pancreatic cancer. Moreover, this biomarker is commonly used in clinical practice.<\/p>\n<\/li>\n \t<li data-start=\"674\" data-end=\"762\" style=\"text-align: justify;\">\n<p data-start=\"676\" data-end=\"762\"><strong data-start=\"676\" data-end=\"690\">creatinine<\/strong> \u2013 Urinary biomarker commonly used as an indicator of kidney function.<\/p>\n<\/li>\n \t<li data-start=\"763\" data-end=\"836\" style=\"text-align: justify;\">\n<p data-start=\"765\" data-end=\"836\"><strong data-start=\"765\" data-end=\"774\">LYVE1<\/strong> \u2013 Urinary protein that may play a role in tumor metastasis.<\/p>\n<\/li>\n \t<li data-start=\"837\" data-end=\"916\" style=\"text-align: justify;\">\n<p data-start=\"839\" data-end=\"916\"><strong data-start=\"839\" data-end=\"848\">REG1B<\/strong> \u2013 Urinary protein possibly associated with pancreas regeneration.<\/p>\n<\/li>\n \t<li data-start=\"917\" data-end=\"1004\" style=\"text-align: justify;\">\n<p data-start=\"919\" data-end=\"1004\"><strong data-start=\"919\" data-end=\"927\">TFF1<\/strong> \u2013 Urinary protein related to repair and regeneration of the urinary tract.<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-df72515 elementor-widget elementor-widget-heading\" data-id=\"df72515\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Unused variables<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f0fe154 elementor-widget elementor-widget-text-editor\" data-id=\"f0fe154\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The following variables were excluded from the model because they do not contribute to diagnosis or are redundant:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bdfb8fc elementor-widget elementor-widget-text-editor\" data-id=\"bdfb8fc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul data-start=\"1146\" data-end=\"1504\">\n \t<li data-start=\"1146\" data-end=\"1185\" style=\"text-align: justify;\">\n<p data-start=\"1148\" data-end=\"1185\"><strong data-start=\"1148\" data-end=\"1161\">sample_id<\/strong> \u2013 Patient identifier.<\/p>\n<\/li>\n \t<li data-start=\"1186\" data-end=\"1233\" style=\"text-align: justify;\">\n<p data-start=\"1188\" data-end=\"1233\"><strong data-start=\"1188\" data-end=\"1205\">sample_origin<\/strong> \u2013 Data collection center.<\/p>\n<\/li>\n \t<li data-start=\"1234\" data-end=\"1277\" style=\"text-align: justify;\">\n<p data-start=\"1236\" data-end=\"1277\"><strong data-start=\"1236\" data-end=\"1254\">patient_cohort<\/strong> \u2013 Cohort assignment.<\/p>\n<\/li>\n \t<li data-start=\"1278\" data-end=\"1347\" style=\"text-align: justify;\">\n<p data-start=\"1280\" data-end=\"1347\"><strong data-start=\"1280\" data-end=\"1289\">stage<\/strong> \u2013 Cancer stage (only available for diagnosed patients).<\/p>\n<\/li>\n \t<li data-start=\"1348\" data-end=\"1419\" style=\"text-align: justify;\">\n<p data-start=\"1350\" data-end=\"1419\"><strong data-start=\"1350\" data-end=\"1377\">benign_sample_diagnosis<\/strong> \u2013 Sub-classification of benign samples.<\/p>\n<\/li>\n \t<li data-start=\"1420\" data-end=\"1504\" style=\"text-align: justify;\">\n<p data-start=\"1422\" data-end=\"1504\"><strong data-start=\"1422\" data-end=\"1431\">REG1A<\/strong> \u2013 Urinary biomarker not available for all samples (replaced by REG1B).<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9f3c94a elementor-widget elementor-widget-heading\" data-id=\"9f3c94a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Target variable<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-649804d elementor-widget elementor-widget-text-editor\" data-id=\"649804d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul data-start=\"1528\" data-end=\"1761\">\n \t<li data-start=\"1528\" data-end=\"1761\" style=\"text-align: justify;\">\n<p data-start=\"1530\" data-end=\"1585\"><strong data-start=\"1530\" data-end=\"1557\">diagnosis (categorical)<\/strong> \u2013 Three possible classes:<\/p>\n\n<ul data-start=\"1588\" data-end=\"1761\">\n \t<li data-start=\"1588\" data-end=\"1631\">\n<p data-start=\"1590\" data-end=\"1631\"><strong data-start=\"1590\" data-end=\"1605\">Control<\/strong> (no pancreatic disease)<\/p>\n<\/li>\n \t<li data-start=\"1634\" data-end=\"1707\">\n<p data-start=\"1636\" data-end=\"1707\"><strong data-start=\"1636\" data-end=\"1672\">Benign hepatobiliary disease<\/strong> (including chronic pancreatitis)<\/p>\n<\/li>\n \t<li data-start=\"1710\" data-end=\"1761\">\n<p data-start=\"1712\" data-end=\"1761\"><strong data-start=\"1712\" data-end=\"1759\">Pancreatic ductal adenocarcinoma (PDAC)<\/strong><\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1a33b2a elementor-widget elementor-widget-heading\" data-id=\"1a33b2a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Instances<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ed418f9 elementor-widget elementor-widget-text-editor\" data-id=\"ed418f9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The dataset\u2019s\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Instances\">instances<\/a> are split into training (60%), validation (20%), and testing (20%) subsets by default.<\/p>\n\n<p style=\"text-align: justify;\">You can adjust them as needed.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-22b5568 elementor-widget elementor-widget-heading\" data-id=\"22b5568\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Variables distribution<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5654f7e elementor-widget elementor-widget-text-editor\" data-id=\"5654f7e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">We can examine variable <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Distributions\">distributios<\/a>; the figure shows a pie chart of patients with and without pancreatic cancer.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ca8795e elementor-widget elementor-widget-image\" data-id=\"ca8795e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"600\" height=\"350\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/1.pancreas-distribution-pie-chart.webp\" class=\"attachment-large size-large wp-image-16557\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/1.pancreas-distribution-pie-chart.webp 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/1.pancreas-distribution-pie-chart-300x175.webp 300w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-37401c0 elementor-widget elementor-widget-text-editor\" data-id=\"37401c0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The dataset was divided into four subsets to evaluate model performance (accuracy and AUC) for different comparisons:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-16b3fc3 elementor-widget elementor-widget-text-editor\" data-id=\"16b3fc3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li data-start=\"211\" data-end=\"324\" style=\"text-align: justify;\">\n<p data-start=\"214\" data-end=\"324\"><strong data-start=\"214\" data-end=\"246\">Control vs. PDAC stages I\u2013II<\/strong> (File: <a href=\"https:\/\/www.neuraldesigner.com\/files\/datasets\/control-vs-PDAC-I_II.csv\">control-vs-PDAC-I_II.csv<\/a>): Healthy individuals vs. PDAC stages I\u2013II.<\/p>\n<\/li>\n \t<li data-start=\"325\" data-end=\"444\" style=\"text-align: justify;\">\n<p data-start=\"328\" data-end=\"444\"><strong data-start=\"328\" data-end=\"362\">Control vs. PDAC stages III\u2013IV<\/strong> (File: <a href=\"https:\/\/www.neuraldesigner.com\/files\/datasets\/control-vs-PDAC-III_IV.csv\">control-vs-PDAC-III_IV.csv<\/a>): Healthy individuals vs. PDAC stages III\u2013IV.<\/p>\n<\/li>\n \t<li data-start=\"445\" data-end=\"572\" style=\"text-align: justify;\">\n<p data-start=\"448\" data-end=\"572\"><strong data-start=\"448\" data-end=\"502\">Benign hepatobiliary diseases vs. PDAC stages I\u2013II<\/strong> (File: <a href=\"https:\/\/www.neuraldesigner.com\/files\/datasets\/benign-vs-PDAC-I_II.csv\">benign-vs-PDAC-I_II.csv<\/a>): Benign cases vs. PDAC stages I\u2013II.<\/p>\n<\/li>\n \t<li data-start=\"573\" data-end=\"706\" style=\"text-align: justify;\">\n<p data-start=\"576\" data-end=\"706\"><strong data-start=\"576\" data-end=\"632\">Benign hepatobiliary diseases vs. PDAC stages III\u2013IV<\/strong> (File: <a href=\"https:\/\/www.neuraldesigner.com\/files\/datasets\/benign-vs-PDAC-III_IV.csv\">benign-vs-PDAC-III_IV.csv<\/a>): Benign cases vs. PDAC stages III\u2013IV.<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-efab158 elementor-widget elementor-widget-text-editor\" data-id=\"efab158\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">Each subset includes a target variable indicating the disease status (0 = control\/benign, 1 = PDAC) and is split into training and testing sets with 50% of the samples each.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-011759d elementor-widget elementor-widget-heading\" data-id=\"011759d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">2.1. Control samples vs. PDAC stages I and II<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b6bee31 elementor-widget elementor-widget-text-editor\" data-id=\"b6bee31\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#InputsTargetsCorrelations\" target=\"_blank\" rel=\"noopener\">input-target correlations <\/a>indicate which factors most influence whether a tumor is malignant or benign and, therefore, are more relevant to our analysis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c410b54 elementor-widget elementor-widget-image\" data-id=\"c410b54\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"600\" height=\"490\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/2.control_I_II_inputs-targets-correlations.png\" class=\"attachment-large size-large wp-image-16553\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/2.control_I_II_inputs-targets-correlations.png 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/2.control_I_II_inputs-targets-correlations-300x245.png 300w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a600a60 elementor-widget elementor-widget-text-editor\" data-id=\"a600a60\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">Here, the most correlated variables with malignant tumors are\u00a0<b>age<\/b>,\u00a0<b>plasma CA19_9<\/b>, and\u00a0<b>sex<\/b>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e507a08 elementor-widget elementor-widget-heading\" data-id=\"e507a08\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">2.2. Control samples vs. PDAC stages III and IV<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-25ca73d elementor-widget elementor-widget-text-editor\" data-id=\"25ca73d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#InputsTargetsCorrelations\" target=\"_blank\" rel=\"noopener\">input-target correlations <\/a>indicate which factors most influence whether a tumor is malignant or benign and, therefore, are more relevant to our analysis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2ee536a elementor-widget elementor-widget-image\" data-id=\"2ee536a\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"600\" height=\"490\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/3.control_III_IV_inputs-targets-correlations-1.png\" class=\"attachment-large size-large wp-image-16552\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/3.control_III_IV_inputs-targets-correlations-1.png 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/3.control_III_IV_inputs-targets-correlations-1-300x245.png 300w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b255cdc elementor-widget elementor-widget-text-editor\" data-id=\"b255cdc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">Here, the most correlated variables with malignant tumors are\u00a0<b>plasma CA129_9<\/b>,\u00a0<b>age<\/b>, and\u00a0<b>sex<\/b>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5567340 elementor-widget elementor-widget-heading\" data-id=\"5567340\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">2.3. Benign hepatobiliary diseases vs. PDAC stages I and II<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b81ae5f elementor-widget elementor-widget-text-editor\" data-id=\"b81ae5f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">Similarly, the next figure illustrates the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#InputsTargetsCorrelations\">input-target correlations<\/a> for all inputs with the target, enabling us to visualize the varying influences of these inputs on the default.<\/p>\n\n<p style=\"text-align: justify;\">The more correlated variables are the biomarkers age, plasma_CA19_9, and REG1B.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ff34400 elementor-widget elementor-widget-heading\" data-id=\"ff34400\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">2.4. Benign hepatobiliary diseases vs. PDAC stages III and IV<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-78bc749 elementor-widget elementor-widget-text-editor\" data-id=\"78bc749\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">Similarly, the next figure illustrates the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#InputsTargetsCorrelations\">input-target correlations<\/a> for all inputs with the target, enabling us to visualize the varying influences of these inputs on the default.<\/p>\n\n<p style=\"text-align: justify;\">The more correlated variables are the biomarkers age, plasma_CA19_9, and creatinine.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eac8a40 elementor-widget elementor-widget-heading\" data-id=\"eac8a40\" data-element_type=\"widget\" id=\"neural_network\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">3. Neural network<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-857f2e1 elementor-widget elementor-widget-text-editor\" data-id=\"857f2e1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">A neural network is an artificial intelligence model inspired by how the human brain processes information.<\/p>\n\n<p style=\"text-align: justify;\">It is organized in layers: the input layer receives the variables, and the output layer provides the probability of belonging to a given class.<\/p>\n\n<p style=\"text-align: justify;\">Trained with historical data, the network learns to recognize patterns and distinguish between categories, offering objective support for decision-making.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-33de5aa elementor-widget elementor-widget-image\" data-id=\"33de5aa\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"751\" height=\"290\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-9.png\" class=\"attachment-large size-large wp-image-19672\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-9.png 751w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-9-300x116.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-9-600x232.png 600w\" sizes=\"(max-width: 751px) 100vw, 751px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-81a6331 elementor-widget elementor-widget-text-editor\" data-id=\"81a6331\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The network combines multiple diagnostic variables to produce a single output: the probability of a malignant tumor, with connections showing each variable\u2019s contribution.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a5ccc7 elementor-widget elementor-widget-heading\" data-id=\"5a5ccc7\" data-element_type=\"widget\" id=\"training_strategy\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">4. Training strategy<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0da68f6 elementor-widget elementor-widget-text-editor\" data-id=\"0da68f6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"168\" data-end=\"543\" style=\"text-align: justify;\">Training a neural network uses a loss function and optimization algorithm to learn from data while avoiding overfitting, ensuring good performance on both training and new cases.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a699a2e elementor-widget elementor-widget-image\" data-id=\"a699a2e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"400\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/5.control_I_II_training_strategy-1.webp\" class=\"attachment-large size-large wp-image-16547\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/5.control_I_II_training_strategy-1.webp 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/5.control_I_II_training_strategy-1-300x200.webp 300w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-54a60c0 elementor-widget elementor-widget-text-editor\" data-id=\"54a60c0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">Now, we calculate the training error of all the cases of this study:<\/p>\n<ul>\n \t<li style=\"text-align: justify;\"><strong>Control samples vs. PDAC stages I and II:<\/strong>\u00a0training error = 0.0968 MSE.<\/li>\n \t<li style=\"text-align: justify;\"><strong>Control samples vs. PDAC stages III and IV:<\/strong>\u00a0training error = 0.105 MSE.<\/li>\n \t<li style=\"text-align: justify;\"><strong>Benign hepatobiliary diseases vs. PDAC stages I and II:<\/strong>\u00a0training error = 0.162 MSE.<\/li>\n \t<li style=\"text-align: justify;\"><strong>Benign hepatobiliary diseases vs. PDAC stages III and IV:<\/strong>\u00a0training error = 0.111 MSE.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5f77d5a elementor-widget elementor-widget-heading\" data-id=\"5f77d5a\" data-element_type=\"widget\" id=\"testing_analysis\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">5. Testing analysis<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0867ce8 elementor-widget elementor-widget-text-editor\" data-id=\"0867ce8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The trained neural network is evaluated on unseen <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\">testing<\/a> data, using tools like the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#RocCurve\">ROC curve<\/a> and its AUC to measure how well the model generalizes and discriminates between classes.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-335b2a9 elementor-widget elementor-widget-heading\" data-id=\"335b2a9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">5.1. Control samples vs. PDAC stages I and II<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-432f45d elementor-widget elementor-widget-heading\" data-id=\"432f45d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">ROC curve<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3193e15 elementor-widget elementor-widget-text-editor\" data-id=\"3193e15\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#RocCurve\">ROC curve<\/a> is a standard tool to evaluate a classification model, showing how well it distinguishes between two classes by comparing predicted results with actual outcomes, such as benign and malignant tumors.<\/p>\n\n<p style=\"text-align: justify;\">A random classifier scores 0.5, while a perfect classifier scores 1.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f059d84 elementor-widget elementor-widget-image\" data-id=\"f059d84\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"400\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/6.control_I_II_ROC_chart-2.webp\" class=\"attachment-large size-large wp-image-16543\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/6.control_I_II_ROC_chart-2.webp 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/6.control_I_II_ROC_chart-2-300x200.webp 300w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1e47fb3 elementor-widget elementor-widget-text-editor\" data-id=\"1e47fb3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The model achieved an <strong>AUC<\/strong> of <strong>0.919<\/strong>, effectively distinguishing healthy individuals, benign hepatobiliary diseases, and pancreatic cancer.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e0b0aae elementor-widget elementor-widget-heading\" data-id=\"e0b0aae\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Confusion matrix<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-63c9d56 elementor-widget elementor-widget-text-editor\" data-id=\"63c9d56\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#ConfusionMatrix\">confusion matrix<\/a> shows the model\u2019s performance by comparing predicted and actual diagnoses. It includes:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2adb5b7 elementor-widget elementor-widget-text-editor\" data-id=\"2adb5b7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li data-start=\"195\" data-end=\"265\" style=\"text-align: justify;\">\n<p data-start=\"197\" data-end=\"265\"><strong data-start=\"197\" data-end=\"215\">True positives<\/strong> \u2013 pancreatic cancer cases correctly identified.<\/p>\n<\/li>\n \t<li data-start=\"266\" data-end=\"382\" style=\"text-align: justify;\">\n<p data-start=\"268\" data-end=\"382\"><strong data-start=\"268\" data-end=\"287\">False positives<\/strong> \u2013 healthy or benign hepatobiliary disease cases incorrectly identified as pancreatic cancer.<\/p>\n<\/li>\n \t<li data-start=\"383\" data-end=\"477\" style=\"text-align: justify;\">\n<p data-start=\"385\" data-end=\"477\"><strong data-start=\"385\" data-end=\"404\">False negatives<\/strong> \u2013 pancreatic cancer cases incorrectly identified as healthy or benign.<\/p>\n<\/li>\n \t<li data-start=\"478\" data-end=\"546\" data-is-last-node=\"\" style=\"text-align: justify;\">\n<p data-start=\"480\" data-end=\"546\" data-is-last-node=\"\"><strong data-start=\"480\" data-end=\"498\">True negatives<\/strong> \u2013 healthy or benign cases correctly identified.<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-be0fe42 elementor-widget elementor-widget-text-editor\" data-id=\"be0fe42\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">For a decision threshold of 0.5, the confusion matrix was:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4d70182 e-flex e-con-boxed e-con e-parent\" data-id=\"4d70182\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-db85398 elementor-widget elementor-widget-text-editor\" data-id=\"db85398\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<table class=\"mi-tabla\" style=\"margin: auto; display: table; border-collapse: collapse;\"><tbody><tr><th>\u00a0<\/th><th>Predicted positive<\/th><th>Predicted negative<\/th><\/tr><tr><th style=\"text-align: left;\">Real positive<\/th><td style=\"text-align: right;\">34<\/td><td style=\"text-align: right;\">6<\/td><\/tr><tr><th style=\"text-align: left;\">Real negative<\/th><td style=\"text-align: right;\">9<\/td><td style=\"text-align: right;\">37<\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-84b5389 elementor-widget elementor-widget-text-editor\" data-id=\"84b5389\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p> style=&#8221;text-align: justify;&#8221;In this case, <strong>82.56%<\/strong> of cases were <strong>correctly classified<\/strong>\u00a0and\u00a0<strong>17.44%<\/strong> were <strong>misclassified<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0cdca38 elementor-widget elementor-widget-heading\" data-id=\"0cdca38\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Binary classification<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2d8c18 elementor-widget elementor-widget-text-editor\" data-id=\"a2d8c18\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The performance of this binary classification model is summarized with standard measures.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e4e2131 elementor-widget elementor-widget-text-editor\" data-id=\"e4e2131\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li data-start=\"218\" data-end=\"275\" style=\"text-align: justify;\">\n<p data-start=\"220\" data-end=\"275\"><strong data-start=\"220\" data-end=\"233\">Accuracy:<\/strong> 82.6% of cases were correctly classified.<\/p>\n<\/li>\n \t<li data-start=\"276\" data-end=\"328\" style=\"text-align: justify;\">\n<p data-start=\"278\" data-end=\"328\"><strong data-start=\"278\" data-end=\"293\">Error rate:<\/strong> 17.4% of cases were misclassified.<\/p>\n<\/li>\n \t<li data-start=\"329\" data-end=\"407\" style=\"text-align: justify;\">\n<p data-start=\"331\" data-end=\"407\"><strong data-start=\"331\" data-end=\"347\">Sensitivity:<\/strong> 79.1% of pancreatic cancer cases were correctly identified.<\/p>\n<\/li>\n \t<li data-start=\"408\" data-end=\"486\" style=\"text-align: justify;\">\n<p data-start=\"410\" data-end=\"486\"><strong data-start=\"410\" data-end=\"426\">Specificity:<\/strong> 86% of healthy or benign cases were correctly identified.<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c87edbb elementor-widget elementor-widget-text-editor\" data-id=\"c87edbb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">These measures indicate that the model is effective at distinguishing pancreatic cancer from healthy or benign hepatobiliary conditions.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7f186d elementor-widget elementor-widget-heading\" data-id=\"a7f186d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">5.2. Control samples vs. PDAC stages III and IV<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-62a1d59 elementor-widget elementor-widget-heading\" data-id=\"62a1d59\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">ROC curve<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-da3038e elementor-widget elementor-widget-text-editor\" data-id=\"da3038e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#RocCurve\">ROC curve<\/a>\u00a0is a standard tool to evaluate a classification model, showing how well it distinguishes between two classes by comparing predicted results with actual outcomes, such as benign and malignant tumors.<\/p>\n\n<p style=\"text-align: justify;\">A random classifier scores 0.5, while a perfect classifier scores 1.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-73227ba elementor-widget elementor-widget-image\" data-id=\"73227ba\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"400\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/7.control_III_IV_ROC_chart-2.webp\" class=\"attachment-large size-large wp-image-16539\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/7.control_III_IV_ROC_chart-2.webp 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/7.control_III_IV_ROC_chart-2-300x200.webp 300w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ea8ea05 elementor-widget elementor-widget-text-editor\" data-id=\"ea8ea05\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The model achieves an <strong>AUC<\/strong> of <strong>0.913<\/strong>, demonstrating excellent performance in distinguishing pancreatic cancer from healthy or benign hepatobiliary conditions.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6361887 elementor-widget elementor-widget-heading\" data-id=\"6361887\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Confusion matrix<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7ef64f6 elementor-widget elementor-widget-text-editor\" data-id=\"7ef64f6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#ConfusionMatrix\">confusion matrix<\/a>\u00a0shows the model\u2019s performance by comparing predicted and actual diagnoses of pancreatic disease. It includes:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-58afa80 elementor-widget elementor-widget-text-editor\" data-id=\"58afa80\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li data-start=\"205\" data-end=\"287\" style=\"text-align: justify;\">\n<p data-start=\"207\" data-end=\"287\"><strong data-start=\"207\" data-end=\"225\">True positives<\/strong> \u2013 patients correctly identified as having pancreatic cancer<\/p>\n<\/li>\n \t<li data-start=\"288\" data-end=\"373\" style=\"text-align: justify;\">\n<p data-start=\"290\" data-end=\"373\"><strong data-start=\"290\" data-end=\"309\">False positives<\/strong> \u2013 patients incorrectly identified as having pancreatic cancer<\/p>\n<\/li>\n \t<li data-start=\"374\" data-end=\"468\" style=\"text-align: justify;\">\n<p data-start=\"376\" data-end=\"468\"><strong data-start=\"376\" data-end=\"395\">False negatives<\/strong> \u2013 patients with pancreatic cancer incorrectly classified as non-cancer<\/p>\n<\/li>\n \t<li data-start=\"469\" data-end=\"553\" data-is-last-node=\"\" style=\"text-align: justify;\">\n<p data-start=\"471\" data-end=\"553\" data-is-last-node=\"\"><strong data-start=\"471\" data-end=\"489\">True negatives<\/strong> \u2013 patients correctly identified as not having pancreatic cancer<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-45efec4 elementor-widget elementor-widget-text-editor\" data-id=\"45efec4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">For a decision threshold of 0.5, the confusion matrix was:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-074522e e-flex e-con-boxed e-con e-parent\" data-id=\"074522e\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-555cdfc elementor-widget elementor-widget-text-editor\" data-id=\"555cdfc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<table class=\"mi-tabla\" style=\"margin: auto; display: table; border-collapse: collapse;\"><tbody><tr><th>\u00a0<\/th><th>Predicted positive<\/th><th>Predicted negative<\/th><\/tr><tr><th style=\"text-align: left;\">Real positive<\/th><td style=\"text-align: right;\">85<\/td><td style=\"text-align: right;\">9<\/td><\/tr><tr><th style=\"text-align: left;\">Real negative<\/th><td style=\"text-align: right;\">7<\/td><td style=\"text-align: right;\">39<\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b027efc elementor-widget elementor-widget-text-editor\" data-id=\"b027efc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">In this case, <strong>88.57%<\/strong> of cases were <strong>correctly classified<\/strong>\u00a0and\u00a0<strong>11.43%<\/strong> were <strong>misclassified<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-90d63d9 elementor-widget elementor-widget-heading\" data-id=\"90d63d9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Binary classification<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-17b54f0 elementor-widget elementor-widget-text-editor\" data-id=\"17b54f0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The performance of this <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#BinaryClassificationTests\">binary classification<\/a> model is summarized with standard measures.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-372af8f elementor-widget elementor-widget-text-editor\" data-id=\"372af8f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li data-start=\"205\" data-end=\"267\" style=\"text-align: justify;\">\n<p data-start=\"207\" data-end=\"267\"><strong data-start=\"207\" data-end=\"220\">Accuracy:<\/strong> 98.5% of patients were correctly classified.<\/p>\n<\/li>\n \t<li data-start=\"268\" data-end=\"321\" style=\"text-align: justify;\">\n<p data-start=\"270\" data-end=\"321\"><strong data-start=\"270\" data-end=\"285\">Error rate:<\/strong> 1.5% of cases were misclassified.<\/p>\n<\/li>\n \t<li data-start=\"322\" data-end=\"409\" style=\"text-align: justify;\">\n<p data-start=\"324\" data-end=\"409\"><strong data-start=\"324\" data-end=\"340\">Sensitivity:<\/strong> 100% of patients with pancreatic cancer were correctly identified.<\/p>\n<\/li>\n \t<li data-start=\"410\" data-end=\"499\" style=\"text-align: justify;\">\n<p data-start=\"412\" data-end=\"499\"><strong data-start=\"412\" data-end=\"428\">Specificity:<\/strong> 98% of patients without pancreatic cancer were correctly identified.<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2ef60f9 elementor-widget elementor-widget-text-editor\" data-id=\"2ef60f9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">These measures indicate that the model is highly effective at distinguishing between patients with and without pancreatic cancer.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c2b8ebb elementor-widget elementor-widget-heading\" data-id=\"c2b8ebb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">5.3. Benign hepatobiliary diseases vs. PDAC stages I and II<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ca9b360 elementor-widget elementor-widget-heading\" data-id=\"ca9b360\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">ROC curve<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c02f998 elementor-widget elementor-widget-text-editor\" data-id=\"c02f998\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#RocCurve\">ROC curve<\/a>\u00a0is a standard tool to evaluate a classification model, showing how well it distinguishes between two classes by comparing predicted results with actual outcomes, such as benign and malignant tumors.<\/p>\n\n<p style=\"text-align: justify;\">A random classifier scores 0.5, while a perfect classifier scores 1.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-94f97f7 elementor-widget elementor-widget-image\" data-id=\"94f97f7\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"400\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/8.benign_I_II_ROC_chart-2.webp\" class=\"attachment-large size-large wp-image-16535\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/8.benign_I_II_ROC_chart-2.webp 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/8.benign_I_II_ROC_chart-2-300x200.webp 300w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9cec05d elementor-widget elementor-widget-text-editor\" data-id=\"9cec05d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The model achieves an <strong>AUC<\/strong> of <strong>0.921<\/strong>, showing excellent performance in distinguishing pancreatic cancer patients from non-patients.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6f92fd3 elementor-widget elementor-widget-heading\" data-id=\"6f92fd3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Confusion matrix<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-88fc070 elementor-widget elementor-widget-text-editor\" data-id=\"88fc070\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#ConfusionMatrix\">confusion matrix<\/a>\u00a0summarizes the model\u2019s performance by comparing predicted and actual patient diagnoses. It includes:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-05c2bae elementor-widget elementor-widget-text-editor\" data-id=\"05c2bae\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li data-start=\"257\" data-end=\"339\" style=\"text-align: justify;\">\n<p data-start=\"259\" data-end=\"339\"><strong data-start=\"259\" data-end=\"277\">True positives<\/strong> \u2013 patients correctly identified as having pancreatic cancer<\/p>\n<\/li>\n \t<li data-start=\"340\" data-end=\"425\" style=\"text-align: justify;\">\n<p data-start=\"342\" data-end=\"425\"><strong data-start=\"342\" data-end=\"361\">False positives<\/strong> \u2013 patients incorrectly identified as having pancreatic cancer<\/p>\n<\/li>\n \t<li data-start=\"426\" data-end=\"517\" style=\"text-align: justify;\">\n<p data-start=\"428\" data-end=\"517\"><strong data-start=\"428\" data-end=\"447\">False negatives<\/strong> \u2013 patients with pancreatic cancer incorrectly identified as healthy<\/p>\n<\/li>\n \t<li data-start=\"518\" data-end=\"581\" data-is-last-node=\"\" style=\"text-align: justify;\">\n<p data-start=\"520\" data-end=\"581\" data-is-last-node=\"\"><strong data-start=\"520\" data-end=\"538\">True negatives<\/strong> \u2013 patients correctly identified as healthy<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-92a5ed7 elementor-widget elementor-widget-text-editor\" data-id=\"92a5ed7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">For a decision threshold of 0.5, the confusion matrix was:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-11f1fde e-flex e-con-boxed e-con e-parent\" data-id=\"11f1fde\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c1582f5 elementor-widget elementor-widget-text-editor\" data-id=\"c1582f5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<table class=\"mi-tabla\" style=\"margin: auto; display: table; border-collapse: collapse;\"><tbody><tr><th>\u00a0<\/th><th>Predicted positive<\/th><th>Predicted negative<\/th><\/tr><tr><th style=\"text-align: left;\">Real positive<\/th><td style=\"text-align: right;\">44<\/td><td style=\"text-align: right;\">5<\/td><\/tr><tr><th style=\"text-align: left;\">Real negative<\/th><td style=\"text-align: right;\">11<\/td><td style=\"text-align: right;\">34<\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8f36695 elementor-widget elementor-widget-text-editor\" data-id=\"8f36695\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">In this case, <strong>82.98%<\/strong> of cases were <strong>correctly classified<\/strong>\u00a0and\u00a0<strong>17.02%<\/strong> were <strong>misclassified<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7302219 elementor-widget elementor-widget-heading\" data-id=\"7302219\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Binary classification<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ccf1f5b elementor-widget elementor-widget-text-editor\" data-id=\"ccf1f5b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The performance of this <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#BinaryClassificationTests\">binary classification<\/a> model can be summarized with standard measures.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b7708dd elementor-widget elementor-widget-text-editor\" data-id=\"b7708dd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li data-start=\"261\" data-end=\"323\" style=\"text-align: justify;\">\n<p data-start=\"263\" data-end=\"323\"><strong data-start=\"263\" data-end=\"276\">Accuracy:<\/strong> 98.5% of patients were correctly classified.<\/p>\n<\/li>\n \t<li data-start=\"324\" data-end=\"377\" style=\"text-align: justify;\">\n<p data-start=\"326\" data-end=\"377\"><strong data-start=\"326\" data-end=\"341\">Error rate:<\/strong> 1.5% of cases were misclassified.<\/p>\n<\/li>\n \t<li data-start=\"378\" data-end=\"465\" style=\"text-align: justify;\">\n<p data-start=\"380\" data-end=\"465\"><strong data-start=\"380\" data-end=\"396\">Sensitivity:<\/strong> 100% of patients with pancreatic cancer were correctly identified.<\/p>\n<\/li>\n \t<li data-start=\"466\" data-end=\"572\" style=\"text-align: justify;\">\n<p data-start=\"468\" data-end=\"572\"><strong data-start=\"468\" data-end=\"484\">Specificity:<\/strong> 98% of healthy patients or those without pancreatic cancer were correctly identified.<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ba1c2f3 elementor-widget elementor-widget-text-editor\" data-id=\"ba1c2f3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">These results indicate that the model is highly effective at distinguishing between patients with and without pancreatic cancer.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-beb8b99 elementor-widget elementor-widget-heading\" data-id=\"beb8b99\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">5.4. Benign hepatobiliary diseases vs. PDAC stages III and IV<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-768e755 elementor-widget elementor-widget-heading\" data-id=\"768e755\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">ROC curve<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-346e403 elementor-widget elementor-widget-text-editor\" data-id=\"346e403\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#RocCurve\">ROC curve<\/a>\u00a0is a standard tool to evaluate a classification model, showing how well it distinguishes between two classes by comparing predicted results with actual outcomes, such as benign and malignant tumors.<\/p>\n\n<p style=\"text-align: justify;\">A random classifier scores 0.5, while a perfect classifier scores 1.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d074154 elementor-widget elementor-widget-image\" data-id=\"d074154\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"400\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/9.benign_III_IV_ROC_chart-2.webp\" class=\"attachment-large size-large wp-image-16531\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/9.benign_III_IV_ROC_chart-2.webp 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/9.benign_III_IV_ROC_chart-2-300x200.webp 300w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7c3e732 elementor-widget elementor-widget-text-editor\" data-id=\"7c3e732\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The model achieves an <strong>AUC<\/strong> of <strong>0.848<\/strong>, demonstrating excellent performance in distinguishing pancreatic cancer patients from non-patients.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fc36514 elementor-widget elementor-widget-heading\" data-id=\"fc36514\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Confusion matrix<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86611b1 elementor-widget elementor-widget-text-editor\" data-id=\"86611b1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#ConfusionMatrix\">confusion matrix<\/a> shows the model\u2019s performance by comparing predicted and actual diagnoses. It includes:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-76072ae elementor-widget elementor-widget-text-editor\" data-id=\"76072ae\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li data-start=\"184\" data-end=\"266\" style=\"text-align: justify;\">\n<p data-start=\"186\" data-end=\"266\"><strong data-start=\"186\" data-end=\"204\">True positives<\/strong> \u2013 patients correctly identified as having pancreatic cancer<\/p>\n<\/li>\n \t<li data-start=\"267\" data-end=\"352\" style=\"text-align: justify;\">\n<p data-start=\"269\" data-end=\"352\"><strong data-start=\"269\" data-end=\"288\">False positives<\/strong> \u2013 patients incorrectly identified as having pancreatic cancer<\/p>\n<\/li>\n \t<li data-start=\"353\" data-end=\"444\" style=\"text-align: justify;\">\n<p data-start=\"355\" data-end=\"444\"><strong data-start=\"355\" data-end=\"374\">False negatives<\/strong> \u2013 patients with pancreatic cancer incorrectly classified as healthy<\/p>\n<\/li>\n \t<li data-start=\"445\" data-end=\"508\" data-is-last-node=\"\" style=\"text-align: justify;\">\n<p data-start=\"447\" data-end=\"508\" data-is-last-node=\"\"><strong data-start=\"447\" data-end=\"465\">True negatives<\/strong> \u2013 patients correctly identified as healthy<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fd91503 elementor-widget elementor-widget-text-editor\" data-id=\"fd91503\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">For a decision threshold of 0.5, the confusion matrix was:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cb699a6 e-flex e-con-boxed e-con e-parent\" data-id=\"cb699a6\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8a3b09e elementor-widget elementor-widget-text-editor\" data-id=\"8a3b09e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<table class=\"mi-tabla\" style=\"margin: auto; display: table; border-collapse: collapse;\"><tbody><tr><th>\u00a0<\/th><th>Predicted positive<\/th><th>Predicted negative<\/th><\/tr><tr><th style=\"text-align: left;\">Real positive<\/th><td style=\"text-align: right;\">47<\/td><td style=\"text-align: right;\">11<\/td><\/tr><tr><th style=\"text-align: left;\">Real negative<\/th><td style=\"text-align: right;\">8<\/td><td style=\"text-align: right;\">23<\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7865e9a elementor-widget elementor-widget-text-editor\" data-id=\"7865e9a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">In this case, <strong>78.65%<\/strong> of cases were <strong>correctly classified<\/strong>\u00a0and\u00a0<strong>21.35%<\/strong> were <strong>misclassified<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-07ae159 elementor-widget elementor-widget-heading\" data-id=\"07ae159\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Binary classification<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f5010d5 elementor-widget elementor-widget-text-editor\" data-id=\"f5010d5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The performance of this binary classification model is summarized with standard measures.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-63e209c elementor-widget elementor-widget-text-editor\" data-id=\"63e209c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li data-start=\"212\" data-end=\"274\" style=\"text-align: justify;\">\n<p data-start=\"214\" data-end=\"274\"><strong data-start=\"214\" data-end=\"227\">Accuracy:<\/strong> 98.5% of patients were correctly classified.<\/p>\n<\/li>\n \t<li data-start=\"275\" data-end=\"328\" style=\"text-align: justify;\">\n<p data-start=\"277\" data-end=\"328\"><strong data-start=\"277\" data-end=\"292\">Error rate:<\/strong> 1.5% of cases were misclassified.<\/p>\n<\/li>\n \t<li data-start=\"329\" data-end=\"416\" style=\"text-align: justify;\">\n<p data-start=\"331\" data-end=\"416\"><strong data-start=\"331\" data-end=\"347\">Sensitivity:<\/strong> 100% of patients with pancreatic cancer were correctly identified.<\/p>\n<\/li>\n \t<li data-start=\"417\" data-end=\"502\" style=\"text-align: justify;\">\n<p data-start=\"419\" data-end=\"502\"><strong data-start=\"419\" data-end=\"435\">Specificity:<\/strong> 98% of healthy or non-cancer patients were correctly identified.<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b36c2f1 elementor-widget elementor-widget-text-editor\" data-id=\"b36c2f1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">These measures indicate that the model is highly effective at distinguishing between patients with and without pancreatic cancer.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-54090ed e-flex e-con-boxed e-con e-parent\" data-id=\"54090ed\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-da88131 elementor-widget elementor-widget-text-editor\" data-id=\"da88131\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<table class=\"mi-tabla\" style=\"margin: auto; display: table; border-collapse: collapse;\">\n<tbody>\n<tr>\n<th>Sensitivity cutoff<\/th>\n<th>Specificity (Control vs I, II)<\/th>\n<th>Specificity (Control vs III, IV)<\/th>\n<\/tr>\n<tr>\n<th style=\"text-align: right;\">0.8<\/th>\n<td style=\"text-align: right;\">0.86<\/td>\n<td style=\"text-align: right;\">0.875<\/td>\n<\/tr>\n<tr>\n<th style=\"text-align: right;\">0.85<\/th>\n<td style=\"text-align: right;\">0.791<\/td>\n<td style=\"text-align: right;\">0.854<\/td>\n<\/tr>\n<tr>\n<th style=\"text-align: right;\">0.9<\/th>\n<td style=\"text-align: right;\">0.744<\/td>\n<td style=\"text-align: right;\">0.833<\/td>\n<\/tr>\n<tr>\n<th style=\"text-align: right;\">0.95<\/th>\n<td style=\"text-align: right;\">0.512<\/td>\n<td style=\"text-align: right;\">0.771<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5ec5bf2 elementor-widget elementor-widget-text-editor\" data-id=\"5ec5bf2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The table shows that specificity decreases as the sensitivity cutoff rises for distinguishing controls from cancer stages I\u2013II and III\u2013IV, reflecting the trade-off between minimizing false negatives and maintaining accuracy.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-42228a1 elementor-widget elementor-widget-text-editor\" data-id=\"42228a1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">Now we treat the case of the benign samples versus pancreatic cancer stages I and II, and III and IV:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-002a48c e-flex e-con-boxed e-con e-parent\" data-id=\"002a48c\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7254cdf elementor-widget elementor-widget-text-editor\" data-id=\"7254cdf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<table class=\"mi-tabla\" style=\"margin: auto; display: table; border-collapse: collapse;\"><tbody><tr><th>Sensitivity cutoff<\/th><th>Specificity (Control vs I, II)<\/th><th>Specificity (Control vs III, IV)<\/th><\/tr><tr><th style=\"text-align: right;\">0.8<\/th><td style=\"text-align: right;\">0.846<\/td><td style=\"text-align: right;\">0.676<\/td><\/tr><tr><th style=\"text-align: right;\">0.85<\/th><td style=\"text-align: right;\">0.769<\/td><td style=\"text-align: right;\">0.647<\/td><\/tr><tr><th style=\"text-align: right;\">0.9<\/th><td style=\"text-align: right;\">0.769<\/td><td style=\"text-align: right;\">0.618<\/td><\/tr><tr><th style=\"text-align: right;\">0.95<\/th><td style=\"text-align: right;\">0.615<\/td><td style=\"text-align: right;\">0.559<\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d226c82 elementor-widget elementor-widget-text-editor\" data-id=\"d226c82\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The table shows that sensitivity decreases at higher specificity cutoffs when distinguishing benign samples from cancer stages, with a more pronounced decline in stages III\u2013IV, indicating better performance for early-stage detection.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fcaab86 elementor-widget elementor-widget-heading\" data-id=\"fcaab86\" data-element_type=\"widget\" id=\"model_deployment\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">6. Model deployment<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-76918aa elementor-widget elementor-widget-text-editor\" data-id=\"76918aa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"129\" data-end=\"249\" style=\"text-align: justify;\">Once validated, the neural network can be deployed to assess new patients, using biomarkers (creatinine, LYVE1, REG1B, TFF1, plasma CA19-9) to estimate the likelihood of PDAC and distinguish healthy, benign, and cancerous cases, providing reliable, non-invasive diagnostic support.<\/p>\n<p data-start=\"129\" data-end=\"249\" style=\"text-align: justify;\">The model can be exported via <a href=\"https:\/\/www.neuraldesigner.com\/my-account\/\">Neural Designer<\/a> for easy clinical integration.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f99c925 e-grid e-con-boxed e-con e-parent\" data-id=\"f99c925\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-02666cd e-con-full e-flex e-con e-child\" data-id=\"02666cd\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-448563a elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"448563a\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/www.neuraldesigner.com\/downloads\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-download\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M216 0h80c13.3 0 24 10.7 24 24v168h87.7c17.8 0 26.7 21.5 14.1 34.1L269.7 378.3c-7.5 7.5-19.8 7.5-27.3 0L90.1 226.1c-12.6-12.6-3.7-34.1 14.1-34.1H192V24c0-13.3 10.7-24 24-24zm296 376v112c0 13.3-10.7 24-24 24H24c-13.3 0-24-10.7-24-24V376c0-13.3 10.7-24 24-24h146.7l49 49c20.1 20.1 52.5 20.1 72.6 0l49-49H488c13.3 0 24 10.7 24 24zm-124 88c0-11-9-20-20-20s-20 9-20 20 9 20 20 20 20-9 20-20zm64 0c0-11-9-20-20-20s-20 9-20 20 9 20 20 20 20-9 20-20z\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5f703dc elementor-widget elementor-widget-heading\" data-id=\"5f703dc\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Conclusions<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1e8fea4 elementor-widget elementor-widget-text-editor\" data-id=\"1e8fea4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"146\" data-end=\"275\" style=\"text-align: justify;\">The pancreatic cancer diagnosis model accurately distinguishes healthy individuals, benign hepatobiliary disease, and PDAC.<\/p>\n<p data-start=\"277\" data-end=\"414\" style=\"text-align: justify;\">Key biomarkers\u2014creatinine, LYVE1, REG1B, TFF1, and plasma CA19-9\u2014align with clinical knowledge, supporting the model\u2019s reliability.<\/p>\n<p data-start=\"416\" data-end=\"592\" style=\"text-align: justify;\">With strong generalization, this neural network offers a non-invasive diagnostic support tool to aid clinicians in early detection and complement traditional methods.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-53edc6a elementor-widget elementor-widget-heading\" data-id=\"53edc6a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">References<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-faf39cb elementor-widget elementor-widget-text-editor\" data-id=\"faf39cb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li style=\"text-align: justify;\">Debernardi S, O&#8217;Brien H, Algahmdi AS, Malats N, Stewart GD, et al. (2020) A combination of urinary biomarker panel and PancRISK score for earlier detection of pancreatic cancer: A case-control study. PLOS Medicine 17(12): e1003489<\/li>\n \t<li style=\"text-align: justify;\">Dataset from: Kaggle: <a href=\"https:\/\/www.kaggle.com\/johnjdavisiv\/urinary-biomarkers-for-pancreatic-cancer\">Urinary biomarkers for pancreatic cancer<\/a>.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f071a43 elementor-widget elementor-widget-heading\" data-id=\"f071a43\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Related posts<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"author":11,"featured_media":1759,"template":"","categories":[29],"tags":[38],"class_list":["post-3513","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-examples","tag-healthcare"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pancreatic cancer prediction with machine learning<\/title>\n<meta name=\"description\" content=\"Build a machine learning model to detect pancreatic cancer using urinary biomarkers with different cancer risk factors.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/pancreatic-cancer\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Urinary biomarkers for pancreatic cancer detection machine learning example\" \/>\n<meta property=\"og:description\" content=\"The objective of this example is to detect pancreatic cancer using urinary biomarkers.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/pancreatic-cancer\/\" \/>\n<meta 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