[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126082-en":3,"doc-seo-126082-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126082,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Prediction of Metabolic Syndrome in Mexicans Using Machine Learning","Metabolic syndrome (MetS) is a major public health challenge in Mexico, strongly linked with overweight and obesity, arterial hypertension, diabetes, abnormal triglycerides, and impaired HDL and total cholesterol. Although predictive tools are valuable for targeted prevention—especially for sedentary higher-education staff—models tailored to under-researched professional groups remain limited. This study builds and compares multiple machine-learning classifiers using labeled data based on IDF and NCEP ATP III standards, evaluating performance with F1-score, accuracy, and AUC-ROC. Random forest, decision trees, and adaptive boosting performed best, and key predictors included triglycerides, glucose, HDL cholesterol, waist-to-height ratio, and BMI.","Prediction of metabolic syndrome in mexicans using machine  \nlearning  \nZaira Pineda-Rico1, Diana Luz de los Angeles Rojas Mendoza1, Ulises Pineda-Rico2  \n1Unidad Academica Multidisciplinaria Region Altiplano, Universidad Autónoma de San Luis Potosi, San Luis Potosi, Mexico 2Facultad de Ciencias, Universidad Autónoma de San Luis Potosi, San Luis Potosi, Mexico  \nArticle history:  \nReceived Nov 30, 2023 Revised Aug 8, 2024 Accepted Aug 30, 2024  \nKeywords:  \nMetabolic syndrome  \nMetabolic syndrome in Mexicans Prediction of metabolic syndrome Predictive models of metabolic syndrome  \nSedentary work habits  \nCorresponding Author:  \nMetabolic syndrome (MetS) is a compelling public health issue in Mexico, with high prevalence rates of overweight, obesity, arterial hypertension, diabetes, high triglycerides, low high-density lipoprotein cholesterol, and high total cholesterol. Despite this, predictive models tailored for under-researched professional groups with sedentary habits are scarce. This study introduces a novel predictive model for MetS using data from the National Center for Health Statistics and a unique dataset of higher education staff. By employing and comparing machine learning algorithms such as decision trees, random forest, artificial neural networks, and adaptive boosting, the research provides new insights into gender and race-specific aspects of MetS. The data was labeled using standards from the International Diabetes Federation and the National Cholesterol Education Program Adult Treatment Panel III to create classification models, which were tested on the higher education staff dataset. Model predictions were assessed using F1-score, accuracy and area under the curve - receiver operating characteristic (AUC-ROC), with random forest, decision tree, and adaptive boosting performing best. The key predictive features identified for MetS prediction include triglycerides, glucose, highdensity lipoprotein cholesterol, waist-to-height ratio, and body mass index.  \nThis is an open access article under the CC BY-SA license.  \nZaira Pineda-Rico  \nUnidad Academica Multidisciplinaria Region Altiplano, Universidad Autónoma de San Luis Potosi Carr. Cedral km 5 y 600, Ejido San José de las Trojes, Matehuala, San Luis Potosi, 78700, Mexico Email: [zaira.pineda@uaslp.mx](zaira.pineda@uaslp.mx)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nMetabolic syndrome (MetS) is a group of factors that can be used as parameters to identify an individual's increased risk for developing chronic diseases, such as type 2 diabetes, coronary diseases, and stroke [1] . MetS can be defined as a “pathologic condition characterized by abdominal obesity, insulin resistance, hypertension, and hyperlipidemia” [2], [3] . According to the World Health Organization (WHO), there are about 422 million people in the world with diabetes and more than 1 billion people with obesity. Hence, halting the rise in diabetes and obesity by 2025 has become a globally agreed upon target.  \nThe National Institute of Diabetes and Digestive and Kidney Diseases stated that the biomarkers used to diagnose MetS change according to the individual’s diet, exercise habits, cultural, and socioeconomic factors [4], [5] . Then, MetS needs to be treated by developing prevention strategies such as detection of risk factors, to reduce the health impact of this condition on the body, as well as on healthcare systems [6]–[8] . Research focused on particular groups has demonstrated a notable correlation between adopting healthy habits and decreasing the risk of developing MetS [9], [10] .  \nIn this context, machine learning (ML) has become a tool that allows the study of large amounts of information to find underlying patterns within the data, that contribute to the occurrence of MetS on specific  \npopulation. ML is a subset of artificial intelligence (AI) technology; it has been described as the science of programming computers to learn from provided information. In recent years, this ty","cbCaidRf6LH6Jr94","https://ap.wps.com/l/cbCaidRf6LH6Jr94","pdf",427023,6,1,"English","en",105,"# Abstract\n# Introduction\n## Metabolic syndrome as a public health issue\n## Diagnosis markers and prevention need\n## Role of machine learning in healthcare","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets the high prevalence of metabolic syndrome in Mexico and the lack of predictive models tailored to under-researched professional groups with sedentary habits.\"},{\"question\":\"Which machine learning models are compared for predicting MetS?\",\"answer\":\"Decision trees, random forest, artificial neural networks, and adaptive boosting are compared using labeled datasets derived from established MetS standards.\"},{\"question\":\"What features are most important for MetS prediction?\",\"answer\":\"The main predictive features identified are triglycerides, glucose, HDL cholesterol, waist-to-height ratio, and body mass index.\"}]","Prediction of Metabolic Syndrome in Mexicans Using Machine Learning | PDF",1785902998,20,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"prediction-of-metabolic-syndrome-in-mexicans-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-metabolic-syndrome-in-mexicans-using-machine-learning/126082/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study address?","Question",{"text":76,"@type":77},"The study targets the high prevalence of metabolic syndrome in Mexico and the lack of predictive models tailored to under-researched professional groups with sedentary habits.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are compared for predicting MetS?",{"text":81,"@type":77},"Decision trees, random forest, artificial neural networks, and adaptive boosting are compared using labeled datasets derived from established MetS standards.",{"name":83,"@type":74,"acceptedAnswer":84},"What features are most important for MetS prediction?",{"text":85,"@type":77},"The main predictive features identified are triglycerides, glucose, HDL cholesterol, waist-to-height ratio, and body mass index.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":29,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":29,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":107,"slug":137},19,"General","general"]