{"id":3481,"date":"2025-09-03T11:12:59","date_gmt":"2025-09-03T09:12:59","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/diabetic-retinopathy-prognosis\/"},"modified":"2026-02-11T11:42:34","modified_gmt":"2026-02-11T10:42:34","slug":"diabetic-retinopathy-risk-prediction","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/diabetic-retinopathy-risk-prediction\/","title":{"rendered":"Diabetic retinopathy risk prediction using machine learning models"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3481\" class=\"elementor elementor-3481\" data-elementor-post-type=\"learning\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-15d79b8f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"15d79b8f\" 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-10e4f5da\" data-id=\"10e4f5da\" 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-42ca38a elementor-widget elementor-widget-heading\" data-id=\"42ca38a\" 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-7d6c223 elementor-widget elementor-widget-text-editor\" data-id=\"7d6c223\" 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=\"134\" data-end=\"345\">Machine learning for diabetic retinopathy risk prediction aids early detection, enabling timely interventions and reducing vision loss. Accurate prognosis from routine clinical and lab data is challenging.<\/p><p style=\"text-align: justify;\" data-start=\"347\" data-end=\"602\">We developed a neural network model using age, systolic blood pressure, and cholesterol from 6,000 patients, achieving an AUC = 0.75 and 74.3% accuracy. This demonstrates its potential as a clinical decision-support tool.<\/p><p style=\"text-align: justify;\" data-start=\"604\" data-end=\"695\">Healthcare professionals can test this approach with Neural Designer<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f64a086 e-con-full e-flex e-con e-parent\" data-id=\"f64a086\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-ce53a36 e-con-full e-flex e-con e-child\" data-id=\"ce53a36\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-63d0c95 e-con-full e-flex e-con e-child\" data-id=\"63d0c95\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-afba420 elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"afba420\" 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>\n\t\t\t\t<div class=\"elementor-element elementor-element-1e91fd7 elementor-widget elementor-widget-heading\" data-id=\"1e91fd7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Contents<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0760afe elementor-widget elementor-widget-text-editor\" data-id=\"0760afe\" 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-d1249aa e-flex e-con-boxed e-con e-parent\" data-id=\"d1249aa\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-84f5bf5 e-grid e-con-full e-con e-child\" data-id=\"84f5bf5\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c046fb7 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"c046fb7\" 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-2c1660d elementor-align-center elementor-widget elementor-widget-button\" data-id=\"2c1660d\" 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-b1cd1eb elementor-align-center elementor-widget elementor-widget-button\" data-id=\"b1cd1eb\" 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-84e6cc3 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"84e6cc3\" 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-8e8e517 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"8e8e517\" 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-8606a31 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"8606a31\" 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\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-11c7263 elementor-widget elementor-widget-heading\" data-id=\"11c7263\" 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-cfa1045 elementor-widget elementor-widget-text-editor\" data-id=\"cfa1045\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"473\" data-end=\"703\">In this example, we develop a binary classification model to estimate the probability that a patient will develop diabetic retinopathy. Specifically, the model predicts whether the outcome is 0 (no retinopathy) or 1 (retinopathy).<\/p><p data-start=\"705\" data-end=\"927\">By framing the problem as a classification task, we allow the neural network to learn patterns associated with higher or lower risk levels. Consequently, clinicians can use the model output as decision-support information.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-db994de elementor-widget elementor-widget-heading\" data-id=\"db994de\" data-element_type=\"widget\" id=\"data_set\" 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-9ae83d3 elementor-widget elementor-widget-text-editor\" data-id=\"9ae83d3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3>Data source<\/h3>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-50dd75f elementor-widget elementor-widget-text-editor\" data-id=\"50dd75f\" 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 diabetic_retinopathy.csv dataset (6,000 instances, 6 variables) for a binary classification problem (target: 0 = no diabetic retinopathy, 1 = diabetic retinopathy).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-61ccf15 e-flex e-con-boxed e-con e-parent\" data-id=\"61ccf15\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-0d45e3f e-con-full e-flex e-con e-child\" data-id=\"0d45e3f\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f59e0ed elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"f59e0ed\" 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\/10\/diabeticretinopathy.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\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5089533 elementor-widget elementor-widget-heading\" data-id=\"5089533\" 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-44eb232 elementor-widget elementor-widget-text-editor\" data-id=\"44eb232\" 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 <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Variables\">variables&#8217;<\/a> information:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-af8dc94 elementor-widget elementor-widget-heading\" data-id=\"af8dc94\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Patient features<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-25580b4 elementor-widget elementor-widget-text-editor\" data-id=\"25580b4\" 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=\"242\" data-end=\"284\" style=\"text-align: justify;\">\n<p data-start=\"244\" data-end=\"284\"><strong data-start=\"244\" data-end=\"251\">age<\/strong> \u2013 Age of the patient in years.<\/p>\n<\/li>\n \t<li data-start=\"285\" data-end=\"339\" style=\"text-align: justify;\">\n<p data-start=\"287\" data-end=\"339\"><strong data-start=\"287\" data-end=\"302\">systolic_bp<\/strong> \u2013 Systolic blood pressure in mmHg.<\/p>\n<\/li>\n \t<li data-start=\"340\" data-end=\"396\" style=\"text-align: justify;\">\n<p data-start=\"342\" data-end=\"396\"><strong data-start=\"342\" data-end=\"358\">diastolic_bp<\/strong> \u2013 Diastolic blood pressure in mmHg.<\/p>\n<\/li>\n \t<li data-start=\"397\" data-end=\"446\" style=\"text-align: justify;\">\n<p data-start=\"399\" data-end=\"446\"><strong data-start=\"399\" data-end=\"414\">cholesterol<\/strong> \u2013 Cholesterol level in mg\/dL.<\/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-fd93e51 elementor-widget elementor-widget-heading\" data-id=\"fd93e51\" 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-7834d46 elementor-widget elementor-widget-text-editor\" data-id=\"7834d46\" 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=\"472\" data-end=\"494\" >retinopathy<\/strong> \u2013 Indicates whether the patient develops diabetic retinopathy.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ceee26b elementor-widget elementor-widget-text-editor\" data-id=\"ceee26b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3>Instances<\/h3>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-41a661e elementor-widget elementor-widget-text-editor\" data-id=\"41a661e\" 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;\">By default, the dataset is split into 60% training (3,600 samples) and 20% each for validation and testing (1,200 samples combined).<\/p>\n\n<p style=\"text-align: justify;\">You can adjust proportions if needed.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3190ec5 elementor-widget elementor-widget-text-editor\" data-id=\"3190ec5\" 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;\">Variable <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Distributions\">distributions<\/a> can be calculated, and the pie chart shows the number of patients with and without diabetic retinopathy.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-69bf7e7 elementor-widget elementor-widget-image\" data-id=\"69bf7e7\" 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=\"505\" height=\"380\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/retinopathy-pie-chart.png\" class=\"attachment-large size-large wp-image-19636\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/retinopathy-pie-chart.png 505w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/retinopathy-pie-chart-300x226.png 300w\" sizes=\"(max-width: 505px) 100vw, 505px\" \/>\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-3d8b68d elementor-widget elementor-widget-text-editor\" data-id=\"3d8b68d\" 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;\">Approximately 48.55% of patients have diabetic retinopathy, while 51.45% do not.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d383765 elementor-widget elementor-widget-text-editor\" data-id=\"d383765\" 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-f162a6b elementor-widget elementor-widget-text-editor\" data-id=\"f162a6b\" 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 patient features most influence the development of diabetic retinopathy and, therefore, are most 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-149e239 elementor-widget elementor-widget-image\" data-id=\"149e239\" 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=\"551\" height=\"314\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/retinopathy-Pearson-correlations-chart.png\" class=\"attachment-large size-large wp-image-19637\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/retinopathy-Pearson-correlations-chart.png 551w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/retinopathy-Pearson-correlations-chart-300x171.png 300w\" sizes=\"(max-width: 551px) 100vw, 551px\" \/>\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-1f6240f elementor-widget elementor-widget-text-editor\" data-id=\"1f6240f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">Here, the most correlated variables with malignant tumors are\u00a0<b>age<\/b>,\u00a0<b>systolic bp<\/b>, and\u00a0<b>cholesterol<\/b>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4381c5d elementor-widget elementor-widget-heading\" data-id=\"4381c5d\" 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-ecba00c elementor-widget elementor-widget-text-editor\" data-id=\"ecba00c\" 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-0bd31b1 elementor-widget elementor-widget-image\" data-id=\"0bd31b1\" 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=\"648\" height=\"370\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-5.png\" class=\"attachment-large size-large wp-image-19638\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-5.png 648w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-5-300x171.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-5-600x343.png 600w\" sizes=\"(max-width: 648px) 100vw, 648px\" \/>\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-91b74eb elementor-widget elementor-widget-text-editor\" data-id=\"91b74eb\" 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 five patient variables\u2014age, systolic and diastolic blood pressure, cholesterol, and ID\u2014to predict the probability of developing diabetic retinopathy, with connections showing each variable&#8217;s contribution.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4b60dfe elementor-widget elementor-widget-heading\" data-id=\"4b60dfe\" 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-9a4a0fd elementor-widget elementor-widget-text-editor\" data-id=\"9a4a0fd\" 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-0857c8a elementor-widget elementor-widget-image\" data-id=\"0857c8a\" 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-5.png\" class=\"attachment-large size-large wp-image-19639\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-5.png 980w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-5-300x159.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-5-768x408.png 768w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-5-600x318.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-4e596aa elementor-widget elementor-widget-text-editor\" data-id=\"4e596aa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"98\" data-end=\"218\" style=\"text-align: justify;\">The model was trained for accuracy and stability, with training and validation errors decreasing steadily (1.147 and 1.156 WSE), indicating effective learning and 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-6492de2 elementor-widget elementor-widget-heading\" data-id=\"6492de2\" data-element_type=\"widget\" id=\"testing_analysis\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">5. Testing analysis<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2fe3861 elementor-widget elementor-widget-text-editor\" data-id=\"2fe3861\" 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 objective of the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\">testing analysis<\/a> is 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-74cf7d4 elementor-widget elementor-widget-heading\" data-id=\"74cf7d4\" 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-ffc4375 elementor-widget elementor-widget-text-editor\" data-id=\"ffc4375\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"90\" data-end=\"338\" style=\"text-align: justify;\">The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#RocCurve\">ROC curve<\/a> is a standard tool to evaluate a classification model, showing how well it distinguishes between two classes by comparing predicted results with actual outcomes, such as patients with or without diabetic retinopathy.<\/p>\n<p data-start=\"340\" data-end=\"410\" style=\"text-align: justify;\">A random classifier scores 0.5, while a perfect classifier scores 1.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-90f395c e-flex e-con-boxed e-con e-parent\" data-id=\"90f395c\" 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-30ece5b elementor-widget elementor-widget-image\" data-id=\"30ece5b\" 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-chart-6.png\" class=\"attachment-large size-large wp-image-19640\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/ROC-chart-6.png 540w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/ROC-chart-6-300x300.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/ROC-chart-6-150x150.png 150w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/ROC-chart-6-100x100.png 100w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/ROC-chart-6-120x120.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-bf775e3 elementor-widget elementor-widget-text-editor\" data-id=\"bf775e3\" 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 <strong>AUC<\/strong> obtained is <strong>0.826<\/strong>, showing that the model performs well at distinguishing between patients with and without diabetic retinopathy.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-87f849c elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"87f849c\" 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-f6b83f8 elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"f6b83f8\" 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:\n<ul><\/p>\n \t<li data-start=\"262\" data-end=\"346\" style=\"text-align: justify;\">\n<p data-start=\"264\" data-end=\"346\"><strong data-start=\"264\" data-end=\"282\">True positives<\/strong> \u2013 patients correctly predicted as having diabetic retinopathy<\/p>\n<\/li>\n \t<li data-start=\"347\" data-end=\"434\" style=\"text-align: justify;\">\n<p data-start=\"349\" data-end=\"434\"><strong data-start=\"349\" data-end=\"368\">False positives<\/strong> \u2013 patients incorrectly predicted as having diabetic retinopathy<\/p>\n<\/li>\n \t<li data-start=\"435\" data-end=\"528\" style=\"text-align: justify;\">\n<p data-start=\"437\" data-end=\"528\"><strong data-start=\"437\" data-end=\"456\">False negatives<\/strong> \u2013 patients with diabetic retinopathy incorrectly predicted as healthy<\/p>\n<\/li>\n \t<li data-start=\"529\" data-end=\"617\" style=\"text-align: justify;\">\n<p data-start=\"531\" data-end=\"617\"><strong data-start=\"531\" data-end=\"549\">True negatives<\/strong> \u2013 patients correctly predicted as not having diabetic retinopathy<\/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-ca367b0 elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"ca367b0\" 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-ce7228a e-con-full e-flex e-con e-child\" data-id=\"ce7228a\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-146fadc elementor-widget elementor-widget-text-editor\" data-id=\"146fadc\" 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;\">464<\/td><td style=\"text-align: right;\">153<\/td><\/tr><tr><th style=\"text-align: left;\">Real negative<\/th><td style=\"text-align: right;\">150<\/td><td style=\"text-align: right;\">433<\/td><\/tr><\/tbody><\/table>\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<div class=\"elementor-element elementor-element-4e73e38 e-flex e-con-boxed e-con e-parent\" data-id=\"4e73e38\" 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-c50b017 elementor-widget elementor-widget-text-editor\" data-id=\"c50b017\" 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>74.75%<\/strong> of cases were correctly classified and <strong>25.25%<\/strong> were misclassified.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ad23254 elementor-widget elementor-widget-heading\" data-id=\"ad23254\" 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-169cdf1 elementor-widget elementor-widget-text-editor\" data-id=\"169cdf1\" 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-d2db5b8 elementor-widget elementor-widget-text-editor\" data-id=\"d2db5b8\" 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=\"254\" data-end=\"316\" style=\"text-align: justify;\">\n<p data-start=\"256\" data-end=\"316\"><strong data-start=\"256\" data-end=\"269\">Accuracy:<\/strong> 74.8% of patients were correctly classified.<\/p>\n<\/li>\n \t<li data-start=\"317\" data-end=\"371\" style=\"text-align: justify;\">\n<p data-start=\"319\" data-end=\"371\"><strong data-start=\"319\" data-end=\"334\">Error rate:<\/strong> 25.3% of cases were misclassified.<\/p>\n<\/li>\n \t<li data-start=\"372\" data-end=\"463\" style=\"text-align: justify;\">\n<p data-start=\"374\" data-end=\"463\"><strong data-start=\"374\" data-end=\"390\">Sensitivity:<\/strong> 75.2% of patients with diabetic retinopathy were correctly identified.<\/p>\n<\/li>\n \t<li data-start=\"464\" data-end=\"558\" style=\"text-align: justify;\">\n<p data-start=\"466\" data-end=\"558\"><strong data-start=\"466\" data-end=\"482\">Specificity:<\/strong> 74.3% of patients without diabetic retinopathy 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-de7d82b elementor-widget elementor-widget-text-editor\" data-id=\"de7d82b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">These measures indicate that the model is effective at distinguishing between patients who will develop diabetic retinopathy and those who will not.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-35cb231 elementor-widget elementor-widget-heading\" data-id=\"35cb231\" data-element_type=\"widget\" id=\"model_deployment\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">6. Model deployment<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e5475c4 elementor-widget elementor-widget-text-editor\" data-id=\"e5475c4\" 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 confirming the neural network\u2019s ability to generalize, the model can be saved for future use in <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/\">deployment<\/a> mode.<\/p><p style=\"text-align: justify;\">This allows the trained network to be applied to new patients, using their clinical and laboratory variables to calculate the probability of developing diabetic retinopathy.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-19c5e0c elementor-widget elementor-widget-html\" data-id=\"19c5e0c\" 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\/diabetic_model.html\" width=\"100%\" height=\"670\" style=\"border:none;\"><\/iframe>\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-440a660 elementor-widget elementor-widget-text-editor\" data-id=\"440a660\" 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.<\/p>\n\n<p style=\"text-align: justify;\">The\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/my-account\/\">Neural Designer<\/a>\u00a0software 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-2461d49 e-flex e-con-boxed e-con e-parent\" data-id=\"2461d49\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-c45e675 e-con-full e-flex e-con e-child\" data-id=\"c45e675\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-dab04ea e-con-full e-flex e-con e-child\" data-id=\"dab04ea\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2d12aa6 elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"2d12aa6\" 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\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-53d8dd4 elementor-widget elementor-widget-heading\" data-id=\"53d8dd4\" 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-6aa9e32 elementor-widget elementor-widget-text-editor\" data-id=\"6aa9e32\" 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=\"89\" data-end=\"254\">The diabetic retinopathy prognosis model, developed with the Coursera dataset, achieved good performance (AUC = 0.75, accuracy = 74.3%) in predicting disease risk.<\/p><p style=\"text-align: justify;\" data-start=\"256\" data-end=\"384\">Key variables\u2014age, systolic blood pressure, and cholesterol\u2014align with clinical knowledge, supporting the model\u2019s reliability.<\/p><p style=\"text-align: justify;\" data-start=\"386\" data-end=\"575\">Its strong generalization makes it a valuable decision-support tool for early risk assessment, complementing clinical evaluations and enabling timely interventions to prevent vision loss.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9620b4c elementor-widget elementor-widget-heading\" data-id=\"9620b4c\" 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-fd3e46e elementor-widget elementor-widget-text-editor\" data-id=\"fd3e46e\" 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;\">The data for this problem has been taken from the <a href=\"https:\/\/www.coursera.org\/learn\/ai-for-medical-prognosis\">Coursera repository<\/a>.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-73a00f0 elementor-widget elementor-widget-heading\" data-id=\"73a00f0\" 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":13,"featured_media":2281,"template":"","categories":[29],"tags":[38],"class_list":["post-3481","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>Diabetic retinopathy risk prediction using machine learning models<\/title>\n<meta name=\"description\" content=\"Build a machine learning model to prognose diabetic retinopathy (diabetic eye disease) conditioned on blood test features.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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