{"id":3470,"date":"2025-09-02T11:12:59","date_gmt":"2025-09-02T09:12:59","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/breast-cancer-mortality\/"},"modified":"2026-02-11T11:57:33","modified_gmt":"2026-02-11T10:57:33","slug":"breast-cancer-mortality","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/breast-cancer-mortality\/","title":{"rendered":"Breast cancer mortality prediction with machine learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3470\" class=\"elementor elementor-3470\" data-elementor-post-type=\"learning\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-55dc6ef4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"55dc6ef4\" 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-96daee0\" data-id=\"96daee0\" 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-4314f77 elementor-widget elementor-widget-heading\" data-id=\"4314f77\" 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-0eb22f7 elementor-widget elementor-widget-text-editor\" data-id=\"0eb22f7\" 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=\"200\" data-end=\"351\">Machine learning can help predict 5-year mortality risk in breast cancer, a heterogeneous disease influenced by clinical features, treatment, and genomic profiles.<\/p><p style=\"text-align: justify;\" data-start=\"200\" data-end=\"351\">Using the METABRIC dataset of 1,880 patients, a neural network achieved an AUC of 0.8 and 81.4% accuracy, demonstrating its potential as a predictive tool.<\/p><p style=\"text-align: justify;\" data-start=\"200\" data-end=\"351\">Clinicians can test this approach by downloading Neural Designer.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-78db361 e-flex e-con-boxed e-con e-parent\" data-id=\"78db361\" 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-ab0ce3a elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"ab0ce3a\" 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-24a3edb elementor-widget elementor-widget-heading\" data-id=\"24a3edb\" 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<div class=\"elementor-element elementor-element-3e8cdc1 e-flex e-con-boxed e-con e-parent\" data-id=\"3e8cdc1\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-d74724f e-grid e-con-full e-con e-child\" data-id=\"d74724f\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c3ec730 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"c3ec730\" 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_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-eab62ff elementor-align-center elementor-widget elementor-widget-button\" data-id=\"eab62ff\" 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-25393e1 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"25393e1\" 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-72bc5f7 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"72bc5f7\" 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-fa59f15 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"fa59f15\" 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_selection\">\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.Model selection<\/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-47b3dd9 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"47b3dd9\" 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\">6.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-46fecdf elementor-align-center elementor-widget elementor-widget-button\" data-id=\"46fecdf\" 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\">7.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\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3003769 elementor-widget elementor-widget-heading\" data-id=\"3003769\" 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-a9bd7e2 elementor-widget elementor-widget-text-editor\" data-id=\"a9bd7e2\" 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=\"144\" style=\"text-align: justify;\"><strong data-start=\"83\" data-end=\"100\">Problem type:<\/strong> Binary <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications#Classification\">classification<\/a> (death or survival)<\/li>\n \t<li data-start=\"83\" data-end=\"144\" style=\"text-align: justify;\"><strong data-start=\"146\" data-end=\"155\">Goal:<\/strong> Model the probability of a patient dying from breast cancer based on clinical data, gene expression, and mutational profile to support clinical decision-making.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-265a06c elementor-widget elementor-widget-heading\" data-id=\"265a06c\" 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-6a5ac76 elementor-widget elementor-widget-text-editor\" data-id=\"6a5ac76\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3>Data source<\/h3><p style=\"text-align: justify;\">The dataset includes 1,880 instances and 689 variables, comprising 26 clinical variables, 489 gene expression variables, and 173 mutation variables.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-45a88b6 e-flex e-con-boxed e-con e-parent\" data-id=\"45a88b6\" 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-7979724 elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"7979724\" 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\/5_years_mortality.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-d3aea32 elementor-widget elementor-widget-heading\" data-id=\"d3aea32\" 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-fef25a3 elementor-widget elementor-widget-text-editor\" data-id=\"fef25a3\" 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 used in the breast cancer mortality prediction model:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b8892fc elementor-widget elementor-widget-heading\" data-id=\"b8892fc\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Patient information<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a72eca0 elementor-widget elementor-widget-text-editor\" data-id=\"a72eca0\" 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=\"40\" data-end=\"87\" style=\"text-align: justify;\">\n<p data-start=\"42\" data-end=\"87\"><strong data-start=\"42\" data-end=\"56\">patient_id<\/strong> \u2013 Unique patient identifier.<\/p>\n<\/li>\n \t<li data-start=\"88\" data-end=\"162\" style=\"text-align: justify;\">\n<p data-start=\"90\" data-end=\"162\"><strong data-start=\"90\" data-end=\"110\">age_at_diagnosis<\/strong> \u2013 Patient\u2019s age at the time of diagnosis (years).<\/p>\n<\/li>\n \t<li data-start=\"163\" data-end=\"259\" style=\"text-align: justify;\">\n<p data-start=\"165\" data-end=\"259\"><strong data-start=\"165\" data-end=\"194\">inferred_menopausal_state<\/strong> \u2013 Inferred menopausal status: premenopausal or postmenopausal.<\/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-5394f5f elementor-widget elementor-widget-heading\" data-id=\"5394f5f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Treatment<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6bab8b7 elementor-widget elementor-widget-text-editor\" data-id=\"6bab8b7\" 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=\"31\" data-end=\"116\" style=\"text-align: justify;\">\n<p data-start=\"33\" data-end=\"116\"><strong data-start=\"33\" data-end=\"59\">type_of_breast_surgery<\/strong> \u2013 Type of breast surgery: mastectomy, lumpectomy, etc.<\/p>\n<\/li>\n \t<li data-start=\"117\" data-end=\"201\" style=\"text-align: justify;\">\n<p data-start=\"119\" data-end=\"201\"><strong data-start=\"119\" data-end=\"144\">chemotherapy (0 or 1)<\/strong> \u2013 Indicates whether the patient received chemotherapy.<\/p>\n<\/li>\n \t<li data-start=\"202\" data-end=\"292\" style=\"text-align: justify;\">\n<p data-start=\"204\" data-end=\"292\"><strong data-start=\"204\" data-end=\"232\">hormone_therapy (0 or 1)<\/strong> \u2013 Indicates whether the patient received hormone therapy.<\/p>\n<\/li>\n \t<li data-start=\"293\" data-end=\"378\" style=\"text-align: justify;\">\n<p data-start=\"295\" data-end=\"378\"><strong data-start=\"295\" data-end=\"321\">radio_therapy (0 or 1)<\/strong> \u2013 Indicates whether the patient received radiotherapy.<\/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-5f30515 elementor-widget elementor-widget-heading\" data-id=\"5f30515\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Tumor characteristics<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2f71881 elementor-widget elementor-widget-text-editor\" data-id=\"2f71881\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul><li style=\"text-align: justify;\"><strong>cancer_type_detailed &#8211;<\/strong> Detailed cancer type (e.g., CDIM, NEUTRAL).<\/li><li style=\"text-align: justify;\"><strong>cellularity &#8211;<\/strong> Tumor cellularity level (e.g., high, low, NA).<\/li><li style=\"text-align: justify;\"><strong>neoplasm_histologic_grade (1-3) &#8211;<\/strong> Histologic grade n of the tumor based on cellular differentiation.<\/li><li style=\"text-align: justify;\"><strong>her2_status (0 or 1) &#8211;<\/strong> HER2 status: negative (0) or positive (1).<\/li><li style=\"text-align: justify;\"><strong>her2 status measured by snp6 &#8211;<\/strong> Method for measuring HER2 status using SNP6.<\/li><li style=\"text-align: justify;\"><strong>er_status (0 or 1)<\/strong> &#8211; Estrogen receptor (ER) status: negative (0) or positive (1).<\/li><li style=\"text-align: justify;\"><strong>er_status_measured_by_ihc &#8211;<\/strong> Method for measuring ER status using IHC.<\/li><li style=\"text-align: justify;\"><strong>pr_status &#8211;<\/strong> Progesterone receptor (PR) status.<\/li><li style=\"text-align: justify;\"><strong>tumor other histologic subtype &#8211;<\/strong> Aditional histologic tumor subtype (e.g., Ductal\/NST).<\/li><li style=\"text-align: justify;\"><strong>integrative_cluster &#8211;<\/strong> Integrative cluster classification of the tumor.<\/li><li style=\"text-align: justify;\"><strong>primary_tumor_laterality &#8211;<\/strong> Primary tumor location (right or left).<\/li><li style=\"text-align: justify;\"><strong>lymph nodes examined positive &#8211;<\/strong> Number of lymph nodes examined and positive.<\/li><li style=\"text-align: justify;\"><span style=\"box-sizing: border-box; margin: 0px; padding: 0px;\"><strong>tumor_size &#8211;<\/strong> Tumor size (mm or cm, depending on the dataset).<\/span><\/li><li style=\"text-align: justify;\"><strong>tumor_stage &#8211;<\/strong> Tumor stage according to TNM or clinical classification.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-63fe049 elementor-widget elementor-widget-heading\" data-id=\"63fe049\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Genomic features<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1616313 elementor-widget elementor-widget-text-editor\" data-id=\"1616313\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul><li style=\"text-align: justify;\" data-start=\"68\" data-end=\"141\"><p data-start=\"70\" data-end=\"141\"><strong data-start=\"70\" data-end=\"88\">mutation_count<\/strong> \u2013 Total number of mutations detected in the tumor.<\/p><\/li><li style=\"text-align: justify;\" data-start=\"142\" data-end=\"227\"><p data-start=\"144\" data-end=\"227\"><strong data-start=\"144\" data-end=\"173\">pam50_+_claudin-1_subtype<\/strong> \u2013 Molecular subtype according to PAM50 + Claudin-1.<\/p><\/li><li style=\"text-align: justify;\" data-start=\"228\" data-end=\"313\"><p data-start=\"230\" data-end=\"313\"><strong data-start=\"230\" data-end=\"259\">3-gene_classifier_subtype<\/strong> \u2013 Molecular subtype according to 3-gene classifier.<\/p><\/li><li style=\"text-align: justify;\" data-start=\"314\" data-end=\"442\"><p data-start=\"316\" data-end=\"442\"><strong data-start=\"316\" data-end=\"347\">nottingham prognostic index<\/strong> \u2013 Nottingham Prognostic Index calculated from tumor size, lymph nodes, and histologic grade.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9477d4e elementor-widget elementor-widget-heading\" data-id=\"9477d4e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Cohort information<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-07f28ac elementor-widget elementor-widget-text-editor\" data-id=\"07f28ac\" 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=\"471\" data-end=\"481\">cohort<\/strong> \u2013 Cohort to which the patient belongs.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7aa3c52 elementor-widget elementor-widget-heading\" data-id=\"7aa3c52\" 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-35e11b7 elementor-widget elementor-widget-text-editor\" data-id=\"35e11b7\" 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=\"548\" data-end=\"561\">mortality<\/strong> \u2013 Can be defined according to the prediction objective (e.g., survival, treatment response, recurrence).<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-33cc27c elementor-widget elementor-widget-text-editor\" data-id=\"33cc27c\" 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\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-73304ca elementor-widget elementor-widget-text-editor\" data-id=\"73304ca\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3>Variables distributions<\/h3>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-20fe1cf elementor-widget elementor-widget-text-editor\" data-id=\"20fe1cf\" 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\">distributions<\/a>; the figure shows a pie chart of patients who survived versus those who did not, summarizing mortality in the dataset.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-77c52e9 elementor-widget elementor-widget-image\" data-id=\"77c52e9\" 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\/breast-cancer-gene-overall-mortality-pie-chart.png\" class=\"attachment-large size-large wp-image-16575\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/breast-cancer-gene-overall-mortality-pie-chart.png 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/breast-cancer-gene-overall-mortality-pie-chart-300x175.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-d9373cb elementor-widget elementor-widget-text-editor\" data-id=\"d9373cb\" 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, 18.52% of the patients did not survive, while approximately 81.48% of the patients survived.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b4caa75 elementor-widget elementor-widget-text-editor\" data-id=\"b4caa75\" 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>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c070455 elementor-widget elementor-widget-text-editor\" data-id=\"c070455\" 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\/data-set#InputsTargetsCorrelations\">input-target correlations<\/a> indicate which factors most influence patient mortality 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-2051474 elementor-widget elementor-widget-image\" data-id=\"2051474\" 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=\"701\" height=\"700\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/overall_mortality-Pearson-correlations-chart.png\" class=\"attachment-large size-large wp-image-17114\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/overall_mortality-Pearson-correlations-chart.png 701w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/overall_mortality-Pearson-correlations-chart-300x300.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/overall_mortality-Pearson-correlations-chart-150x150.png 150w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/overall_mortality-Pearson-correlations-chart-600x599.png 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/overall_mortality-Pearson-correlations-chart-100x100.png 100w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/overall_mortality-Pearson-correlations-chart-120x120.png 120w\" sizes=\"(max-width: 701px) 100vw, 701px\" \/>\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-5d38638 elementor-widget elementor-widget-text-editor\" data-id=\"5d38638\" 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;\"><span style=\"box-sizing: border-box; margin: 0px; padding: 0px;\">Here, the most correlated variables with mortality are <strong>cancer type, detailed<\/strong>\u00a0<strong>tumor other histologic subtype<\/strong>, and\u00a0<strong>HER2 status measured by SNP6<\/strong>.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0fc5e83 elementor-widget elementor-widget-heading\" data-id=\"0fc5e83\" 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-86025f4 elementor-widget elementor-widget-text-editor\" data-id=\"86025f4\" 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-7170ad1 elementor-widget elementor-widget-image\" data-id=\"7170ad1\" 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=\"708\" height=\"450\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-10.png\" class=\"attachment-large size-large wp-image-19676\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-10.png 708w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-10-300x191.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-10-600x381.png 600w\" sizes=\"(max-width: 708px) 100vw, 708px\" \/>\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-e8c4b87 elementor-widget elementor-widget-text-editor\" data-id=\"e8c4b87\" 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 multiple clinical and demographic variables to output the probability of breast cancer mortality, 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-e3ef8c0 elementor-widget elementor-widget-heading\" data-id=\"e3ef8c0\" 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-c378862 elementor-widget elementor-widget-text-editor\" data-id=\"c378862\" 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 involves using 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-1aba6da elementor-widget elementor-widget-image\" data-id=\"1aba6da\" 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=\"800\" height=\"424\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-6.png\" class=\"attachment-large size-large wp-image-19680\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-6.png 980w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-6-300x159.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-6-768x408.png 768w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-6-600x318.png 600w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\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-695c959 elementor-widget elementor-widget-text-editor\" data-id=\"695c959\" 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;\">Since the training error is 0.838 and the selection error is higher (2.569), input selection will be applied to reduce overfitting and improve model generalization.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-97808eb elementor-widget elementor-widget-heading\" data-id=\"97808eb\" data-element_type=\"widget\" id=\"model_selection\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><a href=\"#model_selection\">5. Model selection<\/a><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-39c9e1b elementor-widget elementor-widget-text-editor\" data-id=\"39c9e1b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"235\" data-end=\"483\" style=\"text-align: justify;\">Due to the high number of input neurons and relatively low evaluation metrics, a neuron selection process was performed.<\/p>\n<p data-start=\"485\" data-end=\"811\" style=\"text-align: justify;\">The selection method trains several network architectures with varying numbers of neurons and identifies the configuration that achieves the lowest selection error.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f200e1e elementor-widget elementor-widget-image\" data-id=\"f200e1e\" 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=\"800\" height=\"424\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/selection-errors-plot.png\" class=\"attachment-large size-large wp-image-19682\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/selection-errors-plot.png 980w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/selection-errors-plot-300x159.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/selection-errors-plot-768x408.png 768w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/selection-errors-plot-600x318.png 600w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\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-6e185be elementor-widget elementor-widget-text-editor\" data-id=\"6e185be\" 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;\">After performing input selection, the model was reduced to 35 inputs (removing less relevant features), which lowered the selection error and simplified the network architecture.<\/p>\n\n<p style=\"text-align: justify;\">As shown in the chart, both training error and selection error decrease as the number of inputs is optimized, resulting in a more efficient network with improved performance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e9fdbbb elementor-widget elementor-widget-image\" data-id=\"e9fdbbb\" 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\/breast-cancer-gene-quasi-Newton-method-errors-history.png\" class=\"attachment-large size-large wp-image-16572\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/breast-cancer-gene-quasi-Newton-method-errors-history.png 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/breast-cancer-gene-quasi-Newton-method-errors-history-300x200.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-5093eda elementor-widget elementor-widget-text-editor\" data-id=\"5093eda\" 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 new model was trained for accuracy and stability, with steadily decreasing training and selection errors (0.115 and 0.143 WSE), demonstrating effective learning and strong generalization to new patients.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bbbc6bf elementor-widget elementor-widget-heading\" data-id=\"bbbc6bf\" 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\">6. Testing analysis<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b3e3c2e elementor-widget elementor-widget-text-editor\" data-id=\"b3e3c2e\" 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\">testing analysis<\/a> aims to validate the performance of the generalization properties 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-8380416 elementor-widget elementor-widget-heading\" data-id=\"8380416\" 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-64b0b4f elementor-widget elementor-widget-text-editor\" data-id=\"64b0b4f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<article class=\"text-token-text-primary w-full focus:outline-none scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]\" dir=\"auto\" tabindex=\"-1\" data-turn-id=\"request-68c7c012-8e80-832f-89ac-7accc375c5f1-82\" data-testid=\"conversation-turn-312\" data-scroll-anchor=\"true\" data-turn=\"assistant\" style=\"text-align: justify;\">\n<div class=\"text-base my-auto mx-auto pb-10 [--thread-content-margin:--spacing(4)] thread-sm:[--thread-content-margin:--spacing(6)] thread-lg:[--thread-content-margin:--spacing(16)] px-(--thread-content-margin)\">\n<div class=\"[--thread-content-max-width:40rem] thread-lg:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group\/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn\" tabindex=\"-1\">\n<div class=\"flex max-w-full flex-col grow\">\n<div class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal [.text-message+&amp;]:mt-5\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"f5814bb4-0fba-4b5d-95fe-a814dca9d5e3\" data-message-model-slug=\"gpt-5-mini\">\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[3px]\">\n<div class=\"markdown prose dark:prose-invert w-full break-words light markdown-new-styling\">\n<p data-start=\"80\" data-end=\"288\">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 survival or mortality.<\/p>\n<p data-start=\"290\" data-end=\"358\" data-is-last-node=\"\" data-is-only-node=\"\">A random classifier scores 0.5, while a perfect classifier scores 1.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4cb91d0 elementor-widget elementor-widget-image\" data-id=\"4cb91d0\" 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\/2025\/09\/roc-curve.png\" class=\"attachment-large size-large wp-image-19478\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/roc-curve.png 540w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/roc-curve-300x300.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/roc-curve-150x150.png 150w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/roc-curve-100x100.png 100w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/roc-curve-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-529ed64 elementor-widget elementor-widget-text-editor\" data-id=\"529ed64\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<article class=\"text-token-text-primary w-full focus:outline-none scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]\" dir=\"auto\" tabindex=\"-1\" data-turn-id=\"request-68c7c012-8e80-832f-89ac-7accc375c5f1-83\" data-testid=\"conversation-turn-314\" data-scroll-anchor=\"true\" data-turn=\"assistant\" style=\"text-align: justify;\">\n<div class=\"text-base my-auto mx-auto pb-10 [--thread-content-margin:--spacing(4)] thread-sm:[--thread-content-margin:--spacing(6)] thread-lg:[--thread-content-margin:--spacing(16)] px-(--thread-content-margin)\">\n<div class=\"[--thread-content-max-width:40rem] thread-lg:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group\/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn\" tabindex=\"-1\">\n<div class=\"flex max-w-full flex-col grow\">\n<div class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal [.text-message+&amp;]:mt-5\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"42491674-28c6-4a08-9caf-d60f3937e4f6\" data-message-model-slug=\"gpt-5-mini\">\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[3px]\">\n<div class=\"markdown prose dark:prose-invert w-full break-words light markdown-new-styling\">\n<p data-start=\"82\" data-end=\"238\" data-is-last-node=\"\" data-is-only-node=\"\">The <strong>AUC<\/strong> obtained is <strong>0.8<\/strong>, showing that the model performs exceptionally well at distinguishing between patients who survived and those who did not.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-221adba elementor-widget elementor-widget-heading\" data-id=\"221adba\" 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-e3a73c2 elementor-widget elementor-widget-text-editor\" data-id=\"e3a73c2\" 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 outcomes. It includes:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-08a2cec elementor-widget elementor-widget-text-editor\" data-id=\"08a2cec\" 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=\"190\" data-end=\"255\" style=\"text-align: justify;\">\n<p data-start=\"192\" data-end=\"255\"><strong data-start=\"192\" data-end=\"210\">True positives<\/strong> \u2013 patients correctly predicted as deceased<\/p>\n<\/li>\n \t<li data-start=\"256\" data-end=\"324\" style=\"text-align: justify;\">\n<p data-start=\"258\" data-end=\"324\"><strong data-start=\"258\" data-end=\"277\">False positives<\/strong> \u2013 patients incorrectly predicted as deceased<\/p>\n<\/li>\n \t<li data-start=\"325\" data-end=\"394\" style=\"text-align: justify;\">\n<p data-start=\"327\" data-end=\"394\"><strong data-start=\"327\" data-end=\"346\">False negatives<\/strong> \u2013 patients incorrectly predicted as surviving<\/p>\n<\/li>\n \t<li data-start=\"395\" data-end=\"459\" data-is-last-node=\"\" style=\"text-align: justify;\">\n<p data-start=\"397\" data-end=\"459\" data-is-last-node=\"\"><strong data-start=\"397\" data-end=\"415\">True negatives<\/strong> \u2013 patients correctly predicted as surviving<\/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-67a2633 elementor-widget elementor-widget-text-editor\" data-id=\"67a2633\" 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-763667e e-flex e-con-boxed e-con e-parent\" data-id=\"763667e\" 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-411c163 elementor-widget elementor-widget-text-editor\" data-id=\"411c163\" 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;\">43<\/td><td style=\"text-align: right;\">29<\/td><\/tr><tr><th style=\"text-align: left;\">Real negative<\/th><td style=\"text-align: right;\">41<\/td><td style=\"text-align: right;\">263<\/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-18a9544 elementor-widget elementor-widget-text-editor\" data-id=\"18a9544\" 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>81.4%<\/strong> of cases were <strong>correctly classified<\/strong>&nbsp;and&nbsp;<strong>18.6%<\/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-180d620 elementor-widget elementor-widget-heading\" data-id=\"180d620\" 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-28f8181 elementor-widget elementor-widget-text-editor\" data-id=\"28f8181\" 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;\">Using a classification threshold of 0.3, 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-7936267 elementor-widget elementor-widget-text-editor\" data-id=\"7936267\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul><li style=\"text-align: justify;\" data-start=\"222\" data-end=\"291\"><p data-start=\"224\" data-end=\"291\"><strong data-start=\"224\" data-end=\"237\">Accuracy:<\/strong> 81.4% of patient outcomes were correctly predicted.<\/p><\/li><li style=\"text-align: justify;\" data-start=\"292\" data-end=\"345\"><p data-start=\"294\" data-end=\"345\"><strong data-start=\"294\" data-end=\"309\">Error rate:<\/strong> 18.6% of cases were misclassified.<\/p><\/li><li style=\"text-align: justify;\" data-start=\"346\" data-end=\"419\"><p data-start=\"348\" data-end=\"419\"><strong data-start=\"348\" data-end=\"364\">Sensitivity:<\/strong> 59.7% of deceased patients were correctly identified.<\/p><\/li><li style=\"text-align: justify;\" data-start=\"420\" data-end=\"493\"><p data-start=\"422\" data-end=\"493\"><strong data-start=\"422\" data-end=\"438\">Specificity:<\/strong> 86.5% of surviving patients were correctly identified.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d912ba6 elementor-widget elementor-widget-text-editor\" data-id=\"d912ba6\" 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 predicting patient survival outcomes.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7b1b61 elementor-widget elementor-widget-heading\" data-id=\"a7b1b61\" 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\">7. Model deployment<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-31411ef elementor-widget elementor-widget-text-editor\" data-id=\"31411ef\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"51\" data-end=\"171\" style=\"text-align: justify;\">Once validated, the neural network can be saved for deployment, allowing clinicians to use patients\u2019 clinical data to predict breast cancer mortality.<\/p>\n<p data-start=\"51\" data-end=\"171\" style=\"text-align: justify;\"><a href=\"https:\/\/www.neuraldesigner.com\/my-account\/\">Neural Designer<\/a> automatically exports the trained model, enabling seamless integration as a diagnostic support tool.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e2a7155 e-grid e-con-boxed e-con e-parent\" data-id=\"e2a7155\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-4fb427c e-con-full e-flex e-con e-child\" data-id=\"4fb427c\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e2c909c elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"e2c909c\" 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-7e71220 elementor-widget elementor-widget-heading\" data-id=\"7e71220\" 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-6800bc0 elementor-widget elementor-widget-text-editor\" data-id=\"6800bc0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"118\" data-end=\"253\" style=\"text-align: justify;\">The breast cancer 5-year mortality prediction model (METABRIC dataset) showed strong performance (AUC = 0.8, accuracy = 81.4%).<\/p>\n<p data-start=\"255\" data-end=\"401\" style=\"text-align: justify;\">Key predictors\u2014lymph nodes examined, tumor stage, ER status, and MAPT gene expression\u2014align with clinical knowledge, supporting reliability.<\/p>\n<p data-start=\"403\" data-end=\"533\" style=\"text-align: justify;\">The model can help clinicians assess mortality risk, complement traditional prognostic tools, and guide treatment decisions.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ed64d86 elementor-widget elementor-widget-heading\" data-id=\"ed64d86\" 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-77b458b elementor-widget elementor-widget-text-editor\" data-id=\"77b458b\" 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;\">We have obtained the data for this problem from the <a href=\"https:\/\/www.cbioportal.org\/\">cBioportal Repository Cancer (METABRIC, Nature 2012 &amp; Nat Commun 2016) dataset<\/a>.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-72a5e40 e-grid e-con-boxed e-con e-parent\" data-id=\"72a5e40\" 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-c5b2d96 elementor-widget elementor-widget-text-editor\" data-id=\"c5b2d96\" 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;\">The <a href=\"https:\/\/nemhesys.usal.es\/\">NEMHESYS &#8211; NGS Establishment in Multidisciplinary Healthcare Education System<\/a> project funded the development of this application.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d89a611 elementor-widget elementor-widget-image\" data-id=\"d89a611\" 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=\"431\" height=\"110\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/references-1.png\" class=\"attachment-large size-large wp-image-16570\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/references-1.png 431w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/references-1-300x77.png 300w\" sizes=\"(max-width: 431px) 100vw, 431px\" \/>\t\t\t\t\t\t\t\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-bd82f6b elementor-widget elementor-widget-heading\" data-id=\"bd82f6b\" 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":10,"featured_media":2522,"template":"","categories":[29],"tags":[38],"class_list":["post-3470","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 mortality prediction with machine learning<\/title>\n<meta name=\"description\" content=\"Use machine learning to predict the 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