{"id":3510,"date":"2025-09-03T11:12:58","date_gmt":"2025-09-03T09:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/obesity-level\/"},"modified":"2026-02-11T11:55:08","modified_gmt":"2026-02-11T10:55:08","slug":"obesity-risk-prediction-machine-learning","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/obesity-risk-prediction-machine-learning\/","title":{"rendered":"Obesity risk prediction using machine learning models"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3510\" class=\"elementor elementor-3510\" data-elementor-post-type=\"learning\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2ba88bb e-grid e-con-boxed e-con e-parent\" data-id=\"2ba88bb\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-474f2ab e-con-full e-flex e-con e-child\" data-id=\"474f2ab\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f62580f elementor-widget elementor-widget-heading\" data-id=\"f62580f\" 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-6535cb4 elementor-widget elementor-widget-text-editor\" data-id=\"6535cb4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"196\" data-end=\"359\" style=\"text-align: justify;\">Obesity prediction with machine learning supports healthcare professionals in delivering personalized recommendations and improving clinical decision-making.<\/p>\n<p data-start=\"361\" data-end=\"530\" style=\"text-align: justify;\">As obesity rates continue to rise in Mexico, Peru, and Colombia, accurate assessment is vital to prevent complications such as diabetes and cardiovascular disease.<\/p>\n<p data-start=\"532\" data-end=\"680\" style=\"text-align: justify;\">Using data on lifestyle, anthropometric measures, and family history, we implemented a neural network model that estimates obesity levels.<\/p>\n<p data-start=\"682\" data-end=\"853\" style=\"text-align: justify;\">Trained with the <em data-start=\"699\" data-end=\"719\">ObesityDataSet.csv<\/em>, the model achieved a strong correlation (0.844), showing high potential as a decision-support tool for obesity management.<\/p>\n<p data-start=\"855\" data-end=\"949\" style=\"text-align: justify;\">Healthcare professionals can test this methodology with Neural Designer\u2019s trial version.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-90ca7a6 e-con-full e-flex e-con e-child\" data-id=\"90ca7a6\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-99f8de2 elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"99f8de2\" 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 class=\"elementor-element elementor-element-1b5d50f elementor-widget elementor-widget-heading\" data-id=\"1b5d50f\" 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-1a348e4 elementor-widget elementor-widget-text-editor\" data-id=\"1a348e4\" 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-a4959e2 e-grid e-con-full e-con e-child\" data-id=\"a4959e2\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a5ba71f elementor-align-center elementor-widget elementor-widget-button\" data-id=\"a5ba71f\" 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-1854d83 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"1854d83\" 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-253d533 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"253d533\" 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-a43c833 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"a43c833\" 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-764fba5 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"764fba5\" 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-afa4804 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"afa4804\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#model_deployment\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">6.Model deployment<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f2a8fd6 elementor-widget elementor-widget-heading\" data-id=\"f2a8fd6\" 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-d805fd9 elementor-widget elementor-widget-text-editor\" data-id=\"d805fd9\" 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=\"79\" data-end=\"215\" style=\"text-align: justify;\"><strong data-start=\"79\" data-end=\"96\">Problem type:<\/strong> Multiclass classification (seven obesity levels: Insufficient Weight, Normal Weight, Overweight I\u2013II, Obesity I\u2013III)<\/li>\n \t<li data-start=\"79\" data-end=\"215\" style=\"text-align: justify;\"><strong data-start=\"217\" data-end=\"226\">Goal:<\/strong> Model the probability of each obesity level based on lifestyle, anthropometric, and family history variables using AI and machine learning 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-872ff60 elementor-widget elementor-widget-heading\" data-id=\"872ff60\" 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-bfe75cd elementor-widget elementor-widget-text-editor\" data-id=\"bfe75cd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3>Data source<\/h3>\n<p style=\"text-align: justify;\">The ObesityDataSet.csv dataset contains 2,111 instances and 17 variables for this application.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4b5c5e4 e-con-full e-flex e-con e-child\" data-id=\"4b5c5e4\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-501f249 elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"501f249\" 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\/ObesityDataSet.csv\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-file-download\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224 136V0H24C10.7 0 0 10.7 0 24v464c0 13.3 10.7 24 24 24h336c13.3 0 24-10.7 24-24V160H248c-13.2 0-24-10.8-24-24zm76.45 211.36l-96.42 95.7c-6.65 6.61-17.39 6.61-24.04 0l-96.42-95.7C73.42 337.29 80.54 320 94.82 320H160v-80c0-8.84 7.16-16 16-16h32c8.84 0 16 7.16 16 16v80h65.18c14.28 0 21.4 17.29 11.27 27.36zM377 105L279.1 7c-4.5-4.5-10.6-7-17-7H256v128h128v-6.1c0-6.3-2.5-12.4-7-16.9z\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Dataset<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-969dc77 elementor-widget elementor-widget-heading\" data-id=\"969dc77\" 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-c27b795 elementor-widget elementor-widget-text-editor\" data-id=\"c27b795\" 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-8f18d27 elementor-widget elementor-widget-heading\" data-id=\"8f18d27\" 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-58fbb6a elementor-widget elementor-widget-text-editor\" data-id=\"58fbb6a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul data-start=\"314\" data-end=\"428\">\n \t<li data-start=\"314\" data-end=\"372\" style=\"text-align: justify;\">\n<p data-start=\"316\" data-end=\"372\"><strong data-start=\"316\" data-end=\"345\">gender (1=Female, 0=Male)<\/strong> \u2013 Sex of the individual.<\/p>\n<\/li>\n \t<li data-start=\"373\" data-end=\"428\" style=\"text-align: justify;\">\n<p data-start=\"375\" data-end=\"428\"><strong data-start=\"375\" data-end=\"392\">age (numeric)<\/strong> \u2013 Age of the individual in years.<\/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-22e08c8 elementor-widget elementor-widget-heading\" data-id=\"22e08c8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Anthropometric measurements<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c399b21 elementor-widget elementor-widget-text-editor\" data-id=\"c399b21\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul data-start=\"464\" data-end=\"597\">\n \t<li data-start=\"464\" data-end=\"531\" style=\"text-align: justify;\">\n<p data-start=\"466\" data-end=\"531\"><strong data-start=\"466\" data-end=\"486\">height (numeric)<\/strong> \u2013 Height of the individual in centimeters.<\/p>\n<\/li>\n \t<li data-start=\"532\" data-end=\"597\" style=\"text-align: justify;\">\n<p data-start=\"534\" data-end=\"597\"><strong data-start=\"534\" data-end=\"554\">weight (numeric)<\/strong> \u2013 Weight of the individual in kilograms.<\/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-e880bad elementor-widget elementor-widget-heading\" data-id=\"e880bad\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Family and lifestyle factors<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5553557 elementor-widget elementor-widget-text-editor\" data-id=\"5553557\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul data-start=\"634\" data-end=\"1569\">\n \t<li data-start=\"634\" data-end=\"729\" style=\"text-align: justify;\">\n<p data-start=\"636\" data-end=\"729\"><strong data-start=\"636\" data-end=\"684\">family history with overweight (1=Yes, 0=No)<\/strong> \u2013 Indicates if obesity runs in the family.<\/p>\n<\/li>\n \t<li data-start=\"730\" data-end=\"809\" style=\"text-align: justify;\">\n<p data-start=\"732\" data-end=\"809\"><strong data-start=\"732\" data-end=\"762\">caloric_food (0=Yes, 1=No)<\/strong> \u2013 Frequent consumption of high-caloric food.<\/p>\n<\/li>\n \t<li data-start=\"810\" data-end=\"879\" style=\"text-align: justify;\">\n<p data-start=\"812\" data-end=\"879\"><strong data-start=\"812\" data-end=\"839\">vegetables (1, 2, or 3)<\/strong> \u2013 Frequency of vegetable consumption.<\/p>\n<\/li>\n \t<li data-start=\"880\" data-end=\"938\" style=\"text-align: justify;\">\n<p data-start=\"882\" data-end=\"938\"><strong data-start=\"882\" data-end=\"904\">number_meals (1\u20134)<\/strong> \u2013 Number of main meals per day.<\/p>\n<\/li>\n \t<li data-start=\"939\" data-end=\"1046\" style=\"text-align: justify;\">\n<p data-start=\"941\" data-end=\"1046\"><strong data-start=\"941\" data-end=\"1007\">food_between_meals (1=No, 2=Sometimes, 3=Frequently, 4=Always)<\/strong> \u2013 Consumption of food between meals.<\/p>\n<\/li>\n \t<li data-start=\"1047\" data-end=\"1117\" style=\"text-align: justify;\">\n<p data-start=\"1049\" data-end=\"1117\"><strong data-start=\"1049\" data-end=\"1072\">smoke (0=Yes, 1=No)<\/strong> \u2013 Indicates whether the individual smokes.<\/p>\n<\/li>\n \t<li data-start=\"1118\" data-end=\"1164\" style=\"text-align: justify;\">\n<p data-start=\"1120\" data-end=\"1164\"><strong data-start=\"1120\" data-end=\"1135\">water (1\u20133)<\/strong> \u2013 Daily water consumption.<\/p>\n<\/li>\n \t<li data-start=\"1165\" data-end=\"1239\" style=\"text-align: justify;\">\n<p data-start=\"1167\" data-end=\"1239\"><strong data-start=\"1167\" data-end=\"1193\">calories (0=Yes, 1=No)<\/strong> \u2013 Indicates if calorie intake is monitored.<\/p>\n<\/li>\n \t<li data-start=\"1240\" data-end=\"1296\" style=\"text-align: justify;\">\n<p data-start=\"1242\" data-end=\"1296\"><strong data-start=\"1242\" data-end=\"1260\">activity (0\u20133)<\/strong> \u2013 Frequency of physical activity.<\/p>\n<\/li>\n \t<li data-start=\"1297\" data-end=\"1360\" style=\"text-align: justify;\">\n<p data-start=\"1299\" data-end=\"1360\"><strong data-start=\"1299\" data-end=\"1319\">technology (0\u20132)<\/strong> \u2013 Daily time using technology devices.<\/p>\n<\/li>\n \t<li data-start=\"1361\" data-end=\"1453\" style=\"text-align: justify;\">\n<p data-start=\"1363\" data-end=\"1453\"><strong data-start=\"1363\" data-end=\"1418\">alcohol (1=No, 2=Sometimes, 3=Frequently, 4=Always)<\/strong> \u2013 Alcohol consumption frequency.<\/p>\n<\/li>\n \t<li data-start=\"1454\" data-end=\"1569\" style=\"text-align: justify;\">\n<p data-start=\"1456\" data-end=\"1569\"><strong data-start=\"1456\" data-end=\"1536\">transportation (Automobile, motorbike, bike, public transportation, walking)<\/strong> \u2013 Mode of transportation used.<\/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-1c1ca52 elementor-widget elementor-widget-heading\" data-id=\"1c1ca52\" 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-7b3257f elementor-widget elementor-widget-text-editor\" data-id=\"7b3257f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul data-start=\"1593\" data-end=\"1825\">\n \t<li data-start=\"1593\" data-end=\"1825\" style=\"text-align: justify;\">\n<p data-start=\"1595\" data-end=\"1655\"><strong data-start=\"1595\" data-end=\"1618\">obesity_level (categorical)<\/strong> \u2013 Classification of obesity level: Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II, and Obesity Type III.<\/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-32ed26b elementor-widget elementor-widget-text-editor\" data-id=\"32ed26b\" 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-e94aee3 elementor-widget elementor-widget-text-editor\" data-id=\"e94aee3\" 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-4357adc elementor-widget elementor-widget-text-editor\" data-id=\"4357adc\" 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;\">Variable <a style=\"font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-weight: var( --e-global-typography-text-font-weight ); background-color: #ffffff;\" href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Distributions\">distributions<\/a> can be calculated; the figure shows the number of samples for each obesity level in the dataset.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7b9dfea elementor-widget elementor-widget-image\" data-id=\"7b9dfea\" 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=\"588\" height=\"380\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/obesity_level-distribution-pie-chart.png\" class=\"attachment-large size-large wp-image-17084\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/obesity_level-distribution-pie-chart.png 588w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/obesity_level-distribution-pie-chart-300x194.png 300w\" sizes=\"(max-width: 588px) 100vw, 588px\" \/>\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-23f8ede elementor-widget elementor-widget-text-editor\" data-id=\"23f8ede\" 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;\">Obesity levels show a semi-normal distribution, ranging from 12.88% (Underweight) to 16.63% (Overweight II).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9823842 elementor-widget elementor-widget-text-editor\" data-id=\"9823842\" 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-afdddda elementor-widget elementor-widget-text-editor\" data-id=\"afdddda\" 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 <span style=\"margin: 0px; padding: 0px;\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#InputsTargetsCorrelations\" target=\"_blank\" rel=\"noopener\">input-target correlations<\/a><\/span> indicate which variables most influence the obesity level 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-1185c5a elementor-widget elementor-widget-image\" data-id=\"1185c5a\" 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=\"691\" height=\"700\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/obesity_level-Pearson-correlations-chart.png\" class=\"attachment-large size-large wp-image-17086\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/obesity_level-Pearson-correlations-chart.png 691w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/obesity_level-Pearson-correlations-chart-296x300.png 296w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/obesity_level-Pearson-correlations-chart-600x608.png 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/obesity_level-Pearson-correlations-chart-100x100.png 100w\" sizes=\"(max-width: 691px) 100vw, 691px\" \/>\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-308275f elementor-widget elementor-widget-text-editor\" data-id=\"308275f\" 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 most correlated variables with obesity level are <strong>weight<\/strong>, <strong>gender<\/strong>, and <strong>family history with overweight<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-32494fb elementor-widget elementor-widget-heading\" data-id=\"32494fb\" 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-3c17878 elementor-widget elementor-widget-text-editor\" data-id=\"3c17878\" 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-e2061f5 elementor-widget elementor-widget-image\" data-id=\"e2061f5\" 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=\"800\" height=\"576\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-8.png\" class=\"attachment-large size-large wp-image-19660\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-8.png 847w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-8-300x216.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-8-768x553.png 768w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Network-architecture-8-600x432.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-ced38fe elementor-widget elementor-widget-text-editor\" data-id=\"ced38fe\" 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 eighteen individual variables to predict obesity level, 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-b47f1ae elementor-widget elementor-widget-heading\" data-id=\"b47f1ae\" 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-e218b1e elementor-widget elementor-widget-text-editor\" data-id=\"e218b1e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"164\" data-end=\"445\" 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-e7e0233 elementor-widget elementor-widget-image\" data-id=\"e7e0233\" 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-3.png\" class=\"attachment-large size-large wp-image-18739\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-3.png 980w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-3-300x159.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-3-768x408.png 768w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/Quasi-Newton-method-errors-history-3-600x318.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-d9e0482 elementor-widget elementor-widget-text-editor\" data-id=\"d9e0482\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: justify;\">The model was trained for accuracy and stability, with training and selection errors decreasing steadily (1.147 and 1.142 WSE), indicating effective learning and generalization to new instances.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d319455 elementor-widget elementor-widget-heading\" data-id=\"d319455\" 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-62fc304 elementor-widget elementor-widget-text-editor\" data-id=\"62fc304\" 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 training, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\">testing analysis<\/a> compares the neural network outputs with actual target values on unseen data to validate prediction performance and assess readiness for production.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4c8c068 elementor-widget elementor-widget-text-editor\" data-id=\"4c8c068\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3>Linear regression analysis<\/h3>\n<p style=\"text-align: justify;\">The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#LinearRegressionAnalysis\">linear regression analysis<\/a> illustrates the predicted versus actual obesity levels.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6389c41 elementor-widget elementor-widget-image\" data-id=\"6389c41\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"400\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/5.regression-obe.webp\" class=\"attachment-large size-large wp-image-16664\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/5.regression-obe.webp 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/5.regression-obe-300x200.webp 300w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bef4e6a elementor-widget elementor-widget-text-editor\" data-id=\"bef4e6a\" 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;\">Linear regression of predicted vs. actual obesity levels shows intercept = 0.263, slope = 0.947, and correlation = 0.844, indicating good model performance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8ec8ea0 elementor-widget elementor-widget-heading\" data-id=\"8ec8ea0\" 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-8bd6f02 elementor-widget elementor-widget-text-editor\" data-id=\"8bd6f02\" 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 obesity levels. It includes:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e566886 elementor-widget elementor-widget-text-editor\" data-id=\"e566886\" 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=\"192\" data-end=\"273\" style=\"text-align: justify;\">\n<p data-start=\"194\" data-end=\"273\"><strong data-start=\"194\" data-end=\"213\">True positives:<\/strong> cases correctly classified at the predicted obesity level<\/p>\n<\/li>\n \t<li data-start=\"274\" data-end=\"362\" style=\"text-align: justify;\">\n<p data-start=\"276\" data-end=\"362\"><strong data-start=\"276\" data-end=\"296\">False positives:<\/strong> cases incorrectly classified at a higher or lower obesity level<\/p>\n<\/li>\n \t<li data-start=\"363\" data-end=\"452\" style=\"text-align: justify;\">\n<p data-start=\"365\" data-end=\"452\"><strong data-start=\"365\" data-end=\"385\">False negatives:<\/strong> cases at a given obesity level incorrectly classified as another<\/p>\n<\/li>\n \t<li data-start=\"453\" data-end=\"551\" style=\"text-align: justify;\">\n<p data-start=\"455\" data-end=\"551\"><strong data-start=\"455\" data-end=\"474\">True negatives:<\/strong> cases correctly identified as not belonging to a specific obesity level<\/p>\n<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7b6f96b e-con-full e-flex e-con e-child\" data-id=\"7b6f96b\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1ec0a18 elementor-widget elementor-widget-text-editor\" data-id=\"1ec0a18\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<div class=\"tabla-responsive\">\n<table class=\"mi-tabla\">\n<tbody>\n<tr>\n<th><\/th>\n<th>Predicted normal weight<\/th>\n<th>Predicted Obese I<\/th>\n<th>Predicted Obese II<\/th>\n<th>Predicted Obese III<\/th>\n<th>Predicted Overweight I<\/th>\n<th>Predicted Overweight II<\/th>\n<th>Predicted Underweight<\/th>\n<\/tr>\n<tr>\n<th style=\"text-align: left;\">Real normal weight<\/th>\n<td style=\"text-align: right;\">35<\/td>\n<td style=\"text-align: right;\">3<\/td>\n<td style=\"text-align: right;\">0<\/td>\n<td style=\"text-align: right;\">4<\/td>\n<td style=\"text-align: right;\">1<\/td>\n<td style=\"text-align: right;\">3<\/td>\n<td style=\"text-align: right;\">0<\/td>\n<\/tr>\n<tr>\n<th style=\"text-align: left;\">Real Obese I<\/th>\n<td style=\"text-align: right;\">6<\/td>\n<td style=\"text-align: right;\">27<\/td>\n<td style=\"text-align: right;\">24<\/td>\n<td style=\"text-align: right;\">9<\/td>\n<td style=\"text-align: right;\">6<\/td>\n<td style=\"text-align: right;\">8<\/td>\n<td style=\"text-align: right;\">3<\/td>\n<\/tr>\n<tr>\n<th style=\"text-align: left;\">Real Obese II<\/th>\n<td style=\"text-align: right;\">4<\/td>\n<td style=\"text-align: right;\">12<\/td>\n<td style=\"text-align: right;\">32<\/td>\n<td style=\"text-align: right;\">0<\/td>\n<td style=\"text-align: right;\">1<\/td>\n<td style=\"text-align: right;\">3<\/td>\n<td style=\"text-align: right;\">4<\/td>\n<\/tr>\n<tr>\n<th style=\"text-align: left;\">Real Obese III<\/th>\n<td style=\"text-align: right;\">0<\/td>\n<td style=\"text-align: right;\">0<\/td>\n<td style=\"text-align: right;\">0<\/td>\n<td style=\"text-align: right;\">57<\/td>\n<td style=\"text-align: right;\">0<\/td>\n<td style=\"text-align: right;\">1<\/td>\n<td style=\"text-align: right;\">0<\/td>\n<\/tr>\n<tr>\n<th style=\"text-align: left;\">Real Overweight I<\/th>\n<td style=\"text-align: right;\">7<\/td>\n<td style=\"text-align: right;\">21<\/td>\n<td style=\"text-align: right;\">2<\/td>\n<td style=\"text-align: right;\">8<\/td>\n<td style=\"text-align: right;\">23<\/td>\n<td style=\"text-align: right;\">4<\/td>\n<td style=\"text-align: right;\">2<\/td>\n<\/tr>\n<tr>\n<th style=\"text-align: left;\">Real Overweight II<\/th>\n<td style=\"text-align: right;\">9<\/td>\n<td style=\"text-align: right;\">15<\/td>\n<td style=\"text-align: right;\">5<\/td>\n<td style=\"text-align: right;\">5<\/td>\n<td style=\"text-align: right;\">7<\/td>\n<td style=\"text-align: right;\">15<\/td>\n<td style=\"text-align: right;\">0<\/td>\n<\/tr>\n<tr>\n<th style=\"text-align: left;\">Real Underweight<\/th>\n<td style=\"text-align: right;\">13<\/td>\n<td style=\"text-align: right;\">5<\/td>\n<td style=\"text-align: right;\">0<\/td>\n<td style=\"text-align: right;\">1<\/td>\n<td style=\"text-align: right;\">4<\/td>\n<td style=\"text-align: right;\">0<\/td>\n<td style=\"text-align: right;\">33<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-954b6b8 elementor-widget elementor-widget-text-editor\" data-id=\"954b6b8\" 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 example, <strong>52.61%<\/strong> of cases were <strong>correctly classified<\/strong>\u00a0and\u00a0<strong>47.39%<\/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-2b396b0 elementor-widget elementor-widget-heading\" data-id=\"2b396b0\" 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-1ad1ab4 elementor-widget elementor-widget-text-editor\" data-id=\"1ad1ab4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"599\" data-end=\"738\" style=\"text-align: justify;\">Once validated, the neural network can be saved for <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/\">deployment<\/a>, allowing predictions of obesity level for new individuals based on age, gender, eating habits, and physical activity.<\/p>\n<p data-start=\"599\" data-end=\"738\" style=\"text-align: justify;\">In deployment mode, healthcare professionals can use it as a decision-support tool, with <a href=\"https:\/\/www.neuraldesigner.com\/my-account\/\">Neural Designer<\/a> automatically exporting the trained model for easy integration into clinical or research workflows.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c1c2683 e-con-full e-flex e-con e-child\" data-id=\"c1c2683\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0bc7b15 elementor-widget__width-initial boton_descarga elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"0bc7b15\" 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 class=\"elementor-element elementor-element-edfdb16 elementor-widget elementor-widget-heading\" data-id=\"edfdb16\" 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-10f3701 elementor-widget elementor-widget-text-editor\" data-id=\"10f3701\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"193\" data-end=\"335\" style=\"text-align: justify;\">The\u00a0machine learning model achieved high predictive performance (correlation = 0.844) in estimating obesity levels.<\/p>\n<p data-start=\"337\" data-end=\"527\" style=\"text-align: justify;\">The most influential factors\u2014caloric intake, family history of overweight, and age\u2014are consistent with established medical and nutritional evidence, supporting the model\u2019s reliability.<\/p>\n<p data-start=\"529\" data-end=\"735\" style=\"text-align: justify;\">This tool can help healthcare and nutrition professionals assess obesity more accurately, deliver personalized recommendations, and support effective interventions to improve patient outcomes.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6a63873 elementor-widget elementor-widget-heading\" data-id=\"6a63873\" 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-a85dd72 elementor-widget elementor-widget-text-editor\" data-id=\"a85dd72\" 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:\/\/archive.ics.uci.edu\/ml\/datasets\/Estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition+\">UCI Machine Learning Repository<\/a>.<\/li>\n \t<li style=\"text-align: justify;\">Palechor, F. M., &amp; de la Hoz Manotas, A. (2019). Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico. Data in Brief, 104344.<\/li>\n \t<li style=\"text-align: justify;\">De-La-Hoz-Correa, E., Mendoza Palechor, F., De-La-Hoz-Manotas, A., Morales Ortega, R., &amp; Sanchez Hernandez, A. B. (2019). Obesity level estimation software based on decision trees.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-56dca3e elementor-widget elementor-widget-heading\" data-id=\"56dca3e\" 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<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"author":13,"featured_media":1790,"template":"","categories":[29],"tags":[38],"class_list":["post-3510","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>Obesity risk prediction using machine learning models<\/title>\n<meta name=\"description\" content=\"Build a machine learning model to assess 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