{"id":3468,"date":"2025-08-28T11:13:00","date_gmt":"2025-08-28T09:13:00","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/breast-cancer-diagnosis\/"},"modified":"2026-02-11T15:48:35","modified_gmt":"2026-02-11T14:48:35","slug":"breast-cancer-diagnosis","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/breast-cancer-diagnosis\/","title":{"rendered":"Breast Cancer Diagnosis Machine Learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3468\" class=\"elementor elementor-3468\" data-elementor-post-type=\"learning\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4eef5d5 e-con-full e-flex e-con e-parent\" data-id=\"4eef5d5\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b4a732a elementor-widget elementor-widget-text-editor\" data-id=\"b4a732a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>Introduction<\/h2>\n<p style=\"text-align: justify;\" data-start=\"223\" data-end=\"527\">Breast cancer is one of the most common malignancies, and early detection is essential to improve outcomes.<\/p>\n<p style=\"text-align: justify;\" data-start=\"223\" data-end=\"527\">Fine needle aspiration (FNA) biopsies are commonly used, but interpretation can be complex.<\/p>\n<p style=\"text-align: justify;\" data-start=\"223\" data-end=\"527\">Neural networks analyze cellular features from digitized images to assist clinicians.<\/p>\n<p style=\"text-align: justify;\" data-start=\"223\" data-end=\"527\">Using the University of Wisconsin dataset, our model reached an AUC of 0.997 and 98.5% of accuracy, showing the potential of AI to complement expertise, reduce uncertainty, and improve diagnostic decisions.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ab42a40 elementor-widget elementor-widget-text-editor\" data-id=\"ab42a40\" 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;\">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-1a7b721 e-con-full e-flex e-con e-child\" data-id=\"1a7b721\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-ff17cdd e-con-full e-flex e-con e-child\" data-id=\"ff17cdd\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c09033f elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"c09033f\" 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<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dbd78eb elementor-widget elementor-widget-text-editor\" data-id=\"dbd78eb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>Contents<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fb9e94f elementor-widget elementor-widget-text-editor\" data-id=\"fb9e94f\" 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-d4f16bd e-grid e-con-full e-con e-child\" data-id=\"d4f16bd\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-15ca866 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"15ca866\" 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=\"#aplication_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-1f8081b elementor-align-center elementor-widget elementor-widget-button\" data-id=\"1f8081b\" 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=\"#data_set\">\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-5539b0b elementor-align-center elementor-widget elementor-widget-button\" data-id=\"5539b0b\" 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-47a00e1 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"47a00e1\" 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-3d97605 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"3d97605\" 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-a95ee74 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"a95ee74\" 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<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c123bf2 elementor-widget elementor-widget-text-editor\" data-id=\"c123bf2\" data-element_type=\"widget\" id=\"aplication_type\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>1. Model type<\/h2>\n<ul>\n \t<li data-start=\"60\" data-end=\"129\" style=\"text-align: justify;\"><strong data-start=\"60\" data-end=\"77\">Problem type:<\/strong> Binary <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications#Classification\">classification<\/a> (malignant or benign tumor)<\/li>\n \t<li data-start=\"60\" data-end=\"129\" style=\"text-align: justify;\"><strong data-start=\"131\" data-end=\"140\">Goal:<\/strong> Model the probability of a malignant tumor based on fine needle aspiration (FNA) test features to support 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-6b2a2df elementor-widget elementor-widget-text-editor\" data-id=\"6b2a2df\" data-element_type=\"widget\" id=\"data_set\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>2. Dataset<\/h2>\n<h3>Data source<\/h3>\n<p style=\"text-align: justify;\">The breast_cancer.csv dataset (683 instances, 10 variables) for a binary classification problem (target: 0 or 1).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0eb0f4a e-con-full e-flex e-con e-child\" data-id=\"0eb0f4a\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9aedbe6 elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"9aedbe6\" 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\/breastcancer.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<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a4e4a85 elementor-widget elementor-widget-heading\" data-id=\"a4e4a85\" 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-d993500 elementor-widget elementor-widget-heading\" data-id=\"d993500\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Cell structure<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-19a1a43 elementor-widget elementor-widget-text-editor\" data-id=\"19a1a43\" 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=\"83\" data-end=\"179\" style=\"text-align: justify;\">\n<p data-start=\"85\" data-end=\"179\"><strong data-start=\"85\" data-end=\"104\">clump_thickness<\/strong>\u00a0(1\u201310) \u2013 Benign cells form monolayers; malignant cells form multilayers.<\/p>\n<\/li>\n \t<li data-start=\"180\" data-end=\"254\" style=\"text-align: justify;\">\n<p data-start=\"182\" data-end=\"254\"><strong data-start=\"182\" data-end=\"206\">cell_size_uniformity<\/strong>\u00a0(1\u201310) \u2013 Cancer cells vary in size and shape.<\/p>\n<\/li>\n \t<li data-start=\"255\" data-end=\"330\" style=\"text-align: justify;\">\n<p data-start=\"257\" data-end=\"330\"><strong data-start=\"257\" data-end=\"282\">cell_shape_uniformity<\/strong>\u00a0(1\u201310) \u2013 Cancer cells vary in shape and size.<\/p>\n<\/li>\n \t<li data-start=\"331\" data-end=\"419\" style=\"text-align: justify;\">\n<p data-start=\"333\" data-end=\"419\"><strong data-start=\"333\" data-end=\"364\">single_epithelial_cell_size<\/strong>\u00a0 (1\u201310) \u2013 Enlarged epithelial cells may be malignant.<\/p>\n<\/li>\n \t<li data-start=\"420\" data-end=\"498\" style=\"text-align: justify;\">\n<p data-start=\"422\" data-end=\"498\"><strong data-start=\"422\" data-end=\"437\">bare_nuclei<\/strong>\u00a0(1\u201310) \u2013 Nuclei without cytoplasm, often in benign tumors.<\/p>\n<\/li>\n \t<li data-start=\"499\" data-end=\"590\" style=\"text-align: justify;\">\n<p data-start=\"501\" data-end=\"590\"><strong data-start=\"501\" data-end=\"520\">bland_chromatin<\/strong>\u00a0(1\u201310) \u2013 Uniform chromatin in benign cells; coarse in cancer cells.<\/p>\n<\/li>\n \t<li data-start=\"591\" data-end=\"672\" style=\"text-align: justify;\">\n<p data-start=\"593\" data-end=\"672\"><strong data-start=\"593\" data-end=\"612\">normal_nucleoli<\/strong>\u00a0(1\u201310) \u2013 Small in normal cells, enlarged in cancer cells.<\/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-30b3cfa elementor-widget elementor-widget-heading\" data-id=\"30b3cfa\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Cell behaviour<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8ce3d71 elementor-widget elementor-widget-text-editor\" data-id=\"8ce3d71\" 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=\"703\" data-end=\"779\" style=\"text-align: justify;\">\n<p data-start=\"705\" data-end=\"779\"><strong data-start=\"705\" data-end=\"726\">marginal_adhesion<\/strong>\u00a0(1\u201310) \u2013 Loss of adhesion is a sign of malignancy.<\/p>\n<\/li>\n \t<li data-start=\"780\" data-end=\"853\" style=\"text-align: justify;\">\n<p data-start=\"782\" data-end=\"853\"><strong data-start=\"782\" data-end=\"793\">mitoses<\/strong>\u00a0(1\u201310) \u2013 High values indicate uncontrolled cell division.<\/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-88870ee elementor-widget elementor-widget-heading\" data-id=\"88870ee\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#TargetVariables\">Target variable<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0997063 elementor-widget elementor-widget-text-editor\" data-id=\"0997063\" 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=\"888\" data-end=\"900\">diagnose<\/strong>\u00a0(0 or 1) \u2013 Benign (0) or malignant (1) breast lump.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ef96f11 elementor-widget elementor-widget-text-editor\" data-id=\"ef96f11\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3>Instances<\/h3>\n<p style=\"text-align: justify;\">The dataset\u2019s <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.\n\nYou 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-52bbd88 elementor-widget elementor-widget-text-editor\" data-id=\"52bbd88\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3>Variables distributions<\/h3>\n<p style=\"text-align: justify;\">We can calculate variable <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Distributions\">distributions<\/a>; the figure shows a pie chart of malignant (1) versus benign (0) tumors in the dataset.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-095fa64 elementor-widget elementor-widget-image\" data-id=\"095fa64\" 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=\"476\" height=\"380\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/1.-diagnose-pie-chart-1.png\" class=\"attachment-large size-large wp-image-15574\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/1.-diagnose-pie-chart-1.png 476w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/1.-diagnose-pie-chart-1-300x239.png 300w\" sizes=\"(max-width: 476px) 100vw, 476px\" \/>\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-ab4d654 elementor-widget elementor-widget-text-editor\" data-id=\"ab4d654\" 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;\">As depicted in the image, malignant tumors represent 35% of the samples, and benign tumors represent approximately 65%.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7441a54 elementor-widget elementor-widget-text-editor\" data-id=\"7441a54\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3>Input-target correlations<\/h3>\n<span style=\"box-sizing: border-box; margin: 0px; padding: 0px;\"><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><\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b61e8b8 elementor-widget elementor-widget-image\" data-id=\"b61e8b8\" 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=\"647\" height=\"567\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/2.-diagnose-Pearson-correlations-chart-2.png\" class=\"attachment-large size-large wp-image-15613\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/2.-diagnose-Pearson-correlations-chart-2.png 647w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/2.-diagnose-Pearson-correlations-chart-2-300x263.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/2.-diagnose-Pearson-correlations-chart-2-600x526.png 600w\" sizes=\"(max-width: 647px) 100vw, 647px\" \/>\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-0741891 elementor-widget elementor-widget-text-editor\" data-id=\"0741891\" 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>cell size uniformity<\/b>,\u00a0<b>cell shape uniformity<\/b>, and\u00a0<b>bare nuclei<\/b>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e2cf752 elementor-widget elementor-widget-text-editor\" data-id=\"e2cf752\" data-element_type=\"widget\" id=\"neural_network\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>3. Neural network<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-394f160 elementor-widget elementor-widget-text-editor\" data-id=\"394f160\" 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.\n\nIt is organized in layers: the input layer receives the variables, and the output layer provides the probability of belonging to a given class.\n\nThe network uses historical data to learn patterns distinguishing benign from malignant tumors.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2f3879b elementor-widget elementor-widget-image\" data-id=\"2f3879b\" 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=\"752\" height=\"770\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/3.-Network-architecture-1.png\" class=\"attachment-large size-large wp-image-15611\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/3.-Network-architecture-1.png 752w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/3.-Network-architecture-1-293x300.png 293w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/3.-Network-architecture-1-600x614.png 600w\" sizes=\"(max-width: 752px) 100vw, 752px\" \/>\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-f2bc90c elementor-widget elementor-widget-text-editor\" data-id=\"f2bc90c\" 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 uses nine diagnostic variables to output the probability of a malignant tumor, with connections showing each variable\u2019s contribution to the prediction.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9aeb411 elementor-widget elementor-widget-text-editor\" data-id=\"9aeb411\" data-element_type=\"widget\" id=\"training_strategy\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>4. Training strategy<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a1549aa elementor-widget elementor-widget-text-editor\" data-id=\"a1549aa\" 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;\">Training a neural network uses a loss function to measure errors and an optimization algorithm to adjust the model, ensuring it learns from data while avoiding overfitting for good performance on new cases.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-93f5545 elementor-widget elementor-widget-image\" data-id=\"93f5545\" 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=\"640\" height=\"440\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/4.-Quasi-Newton-method-errors-history-2.png\" class=\"attachment-large size-large wp-image-15789\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/4.-Quasi-Newton-method-errors-history-2.png 640w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/4.-Quasi-Newton-method-errors-history-2-300x206.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/4.-Quasi-Newton-method-errors-history-2-600x413.png 600w\" sizes=\"(max-width: 640px) 100vw, 640px\" \/>\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-0289dfc elementor-widget elementor-widget-text-editor\" data-id=\"0289dfc\" 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 was trained to minimize errors while avoiding overfitting, achieving stable performance on new cases (training error 0.054, validation error 0.072).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f8b5838 elementor-widget elementor-widget-text-editor\" data-id=\"f8b5838\" data-element_type=\"widget\" id=\"testing_analysis\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>5. Testing Analysis<\/h2>\n<p style=\"text-align: justify;\">The objective of the\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\">testing analysis<\/a>\u00a0is to validate the generalization performance of the trained neural network.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8cef445 elementor-widget elementor-widget-heading\" data-id=\"8cef445\" 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-b2f5ff9 elementor-widget elementor-widget-text-editor\" data-id=\"b2f5ff9\" 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.\n\nA 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-f46ae84 elementor-widget elementor-widget-image\" data-id=\"f46ae84\" 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=\"540\" height=\"540\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/7.-ROC-chart-1.png\" class=\"attachment-large size-large wp-image-15622\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/7.-ROC-chart-1.png 540w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/7.-ROC-chart-1-300x300.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/7.-ROC-chart-1-150x150.png 150w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/7.-ROC-chart-1-100x100.png 100w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/7.-ROC-chart-1-120x120.png 120w\" sizes=\"(max-width: 540px) 100vw, 540px\" \/>\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-c0157f1 elementor-widget elementor-widget-text-editor\" data-id=\"c0157f1\" 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 data-start=\"2263\" data-end=\"2279\">AUC of 0.997<\/strong>, indicating excellent discrimination between benign and malignant tumors.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-83e8fc0 elementor-widget elementor-widget-heading\" data-id=\"83e8fc0\" 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-ae66949 elementor-widget elementor-widget-text-editor\" data-id=\"ae66949\" 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:\n<ul>\n \t<li><strong>true positives &#8211; <\/strong>tumors correctly identified as malignant<\/li>\n \t<li><strong>false positives &#8211; <\/strong>benign tumors incorrectly identified as malignant<\/li>\n \t<li><strong>false negatives &#8211; <\/strong>malignant tumors incorrectly identified as benign<\/li>\n \t<li><strong>true negatives &#8211; <\/strong>tumors correctly identified as benign<\/li>\n<\/ul><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5bf4df7 elementor-widget elementor-widget-text-editor\" data-id=\"5bf4df7\" 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-e91466f e-flex e-con-boxed e-con e-child\" data-id=\"e91466f\" 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-970dd8b elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"970dd8b\" 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><\/th>\n<th>Predicted positive<\/th>\n<th>Predicted negative<\/th>\n<\/tr>\n<tr>\n<th style=\"text-align: left;\">Real positive<\/th>\n<td style=\"text-align: right;\">47<\/td>\n<td style=\"text-align: right;\">0<\/td>\n<\/tr>\n<tr>\n<th style=\"text-align: left;\">Real negative<\/th>\n<td style=\"text-align: right;\">2<\/td>\n<td style=\"text-align: right;\">87<\/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-d31d1c8 elementor-widget elementor-widget-text-editor\" data-id=\"d31d1c8\" 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>98.53%<\/strong> of cases were <strong>correctly classified<\/strong> and\u00a0 <strong>1.47%<\/strong>\u00a0were <strong>misclassified<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5e923ec elementor-widget elementor-widget-text-editor\" data-id=\"5e923ec\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3>Binary classification<\/h3>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2f0f4f elementor-widget elementor-widget-text-editor\" data-id=\"a2f0f4f\" 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-555a1fc elementor-widget elementor-widget-text-editor\" data-id=\"555a1fc\" 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=\"221\" data-end=\"281\" style=\"text-align: justify;\">\n<p data-start=\"223\" data-end=\"281\"><strong data-start=\"223\" data-end=\"236\">Accuracy:<\/strong> 98.5% of tumors were correctly classified.<\/p>\n<\/li>\n \t<li data-start=\"282\" data-end=\"335\" style=\"text-align: justify;\">\n<p data-start=\"284\" data-end=\"335\"><strong data-start=\"284\" data-end=\"299\">Error rate:<\/strong> 1.5% of cases were misclassified.<\/p>\n<\/li>\n \t<li data-start=\"336\" data-end=\"408\" style=\"text-align: justify;\">\n<p data-start=\"338\" data-end=\"408\"><strong data-start=\"338\" data-end=\"354\">Sensitivity:<\/strong> 100% of malignant tumors were correctly identified.<\/p>\n<\/li>\n \t<li data-start=\"409\" data-end=\"477\" style=\"text-align: justify;\">\n<p data-start=\"411\" data-end=\"477\"><strong data-start=\"411\" data-end=\"427\">Specificity:<\/strong> 98% of benign tumors 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-3698cda elementor-widget elementor-widget-text-editor\" data-id=\"3698cda\" 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 correctly identifies nearly all malignant and benign tumors, confirming its high diagnostic performance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4f89682 elementor-widget elementor-widget-text-editor\" data-id=\"4f89682\" data-element_type=\"widget\" id=\"model_deployment\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>6. Model deployment<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f1723d7 elementor-widget elementor-widget-text-editor\" data-id=\"f1723d7\" 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;\">Once validated, the model can be deployed to predict malignancy probabilities for new patients.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5fbd2b6 elementor-widget__width-initial elementor-widget elementor-widget-html\" data-id=\"5fbd2b6\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t\t<iframe src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/10\/urinary_inflammation_model.html\" height=\"700\" style=\"border:none;\"><\/iframe>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-76e3d7e elementor-widget elementor-widget-text-editor\" data-id=\"76e3d7e\" 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 deployment mode, healthcare professionals can use the model as a reliable diagnostic support tool for classifying new patients.\n\nThe <a href=\"https:\/\/www.neuraldesigner.com\/my-account\/\">Neural Designer<\/a> software exports the trained model automatically, making it easy to integrate into clinical practice.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-81a97c9 e-grid e-con-full e-con e-child\" data-id=\"81a97c9\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-359f7a9 e-con-full e-flex e-con e-child\" data-id=\"359f7a9\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-dc16e46 elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"dc16e46\" 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<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f85ce45 elementor-widget elementor-widget-text-editor\" data-id=\"f85ce45\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>Conclusions<\/h2>\n<p data-start=\"235\" data-end=\"478\" style=\"text-align: justify;\">The breast cancer diagnostic model, developed with the University of Wisconsin dataset, showed excellent performance (AUC = 0.997, accuracy = 98.5%) in distinguishing benign from malignant tumors.<\/p>\n<p data-start=\"235\" data-end=\"478\" style=\"text-align: justify;\">Key features\u2014cell size and shape uniformity, and bare nuclei\u2014align with pathological criteria, confirming clinical validity.<\/p>\n<p data-start=\"235\" data-end=\"478\" style=\"text-align: justify;\">With strong generalization capacity, this neural network can serve as a valuable decision-support tool, enhancing early detection, complementing FNA biopsy interpretation, and improving diagnostic accuracy in clinical practice.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cfa3605 elementor-widget elementor-widget-text-editor\" data-id=\"cfa3605\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>References<\/h2>\n<ul>\n \t<li style=\"text-align: justify;\">We have obtained the data for this problem from the\u00a0<a href=\"https:\/\/archive.ics.uci.edu\/ml\/datasets\/breast+cancer+wisconsin+(original)\">UCI Machine Learning Repository<\/a>.<\/li>\n \t<li style=\"text-align: justify;\">Wolberg, W.H., &amp; Mangasarian, O.L. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In Proceedings of the National Academy of Sciences, 87, 9193&#8211;9196.<\/li>\n \t<li style=\"text-align: justify;\">Zhang, J. (1992). Selecting typical instances in instance-based learning. In Proceedings of the Ninth International Machine Learning Conference (pp. 470&#8211;479). Aberdeen, Scotland: Morgan Kaufmann.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8c13cf9 elementor-widget elementor-widget-text-editor\" data-id=\"8c13cf9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2>Related posts<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"author":13,"featured_media":2529,"template":"","categories":[29],"tags":[38],"class_list":["post-3468","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>Breast Cancer Diagnosis Machine Learning<\/title>\n<meta name=\"description\" content=\"Build a machine learning model for breast cancer diagnosis based on whether a lump could be malignant (cancerous) or benign (non-cancerous).\" 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