[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122975-en":3,"doc-seo-122975-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"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},122975,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Predicting Obesity Risk Through Lifestyle Habits - A Comparative Analysis of Machine Learning Models","This paper addresses the growing global burden of obesity by targeting the early identification of individuals at high risk for timely, personalized interventions. Using the UCI Machine Learning Repository with 2,111 subjects from diverse regions, obesity levels are labeled according to the Mexican Normativity in alignment with CDC standards. Multiple classifiers—including enhanced Logistic regression, LogitBoost, Random Forests, XGBoost, SVM, Naive Bayes, and KNN—predict obesity from lifestyle habits while excluding direct height and weight parameters. Cross-validation highlights major contributors such as between-meal food intake and family obesity history, with LogitBoost best among boosting models.","Predicting Obesity Risk Through Lifestyle Habits: A Comparative Analysis of Machine Learning Models  \nXiaotian Wang*  \nSchool of Mathematical Sciences, University of Southampton, University Road, Southampton, SO17 1BJ, UK  \nAbstract. This paper explores the escalating global concern of obesity, emphasizing the significance of identifying high-risk individuals to deploy targeted intervention strategies. Employing the University of California, Irvine (UCI) Machine Learning Repository dataset of 2,111 subjects from diverse regions, the classification of obesity levels was based on the Mexican Normativity, which closely aligns with Centers for Disease Control and Prevention (CDC) standards. The primary objective was to assess the predictive capabilities of an array of machine learning models in forecasting obesity levels based on lifestyle habits, excluding direct parameters like height and weight. An enhanced Logistic regression model, LogitBoost model, Random Forests, XGBoost, Support Vector Machines (SVM), Naive Bayes classifiers, and KNearest Neighbors (KNN) were employed for analysis. Through cross-validation, this research determined the hierarchy of factors contributing to obesity, spotlighting variables like 'Consumption of food between meals' and 'Obesity among family members' as major contributors. The results indicate that while LogitBoost performed optimally among Boost algorithms, its performance was slightly below traditional methods. This study's unique approach of emphasizing lifestyle predictors, excluding direct height and weight variables, underscores the need for targeted, personalized intervention strategies in managing the global obesity epidemic.  \n1 lntroduction  \nThe term \"obesity\" generally refers to individuals with an excess accumulation of body fat. In epidemiological research, the commonly employed method for assessing and classifying obesity involves estimating body fat through height and weight measurements [1] . Among the prevalent techniques, the Body Mass Index (BMI) stands as the most credible estimate at present. It is computed asthe quotient of an individual's weight (measured in kilograms) to the square of their height (denoted in meters)  \n[2] . The categorization of obesity varies across different standards [3] . According to the World Health Organization (WHO) criteria, individuals with a Body Mass Index (BMI) ranging from 25 to 30 are classified as overweight, while those with a BMI exceeding 30 are categorized as obese [2] . Conversely, the National Health Service (NHS) suggests distinct thresholds of 23 and 27.5 for defining overweight and obesity, respectively, particularly applicable to regions such as South Asia and China [4] . In parallel with WHO guidelines, the Centers for Disease Control and Prevention (CDC) utilize analogous standards to identify overweight individuals. However, the CDC introduces a tripartite classification for obesity: Grade 1-BMI between 30 and \u003C 35; Grade 2 -BMI between 35 and \u003C 40; Grade 3-BMI of 40 or higher. The third grade of obesity, often denoted as \"severe\"  \nobesity, is occasionally categorized as the most critical stage [5] .  \nObesity engenders adverse repercussions on both economic and health fronts. Firstly, one of the most frequently cited economic consequences of obesity is the expenditure on healthcare [6] . Research conducted by Sturm et al reveals that medical costs for obese individuals are 36% higher per annum when compared to those of individuals with a normal weight. Furthermore, Bahia et al. computed that over a three-year span from 2008 to 2010, healthcare costs resulting from obesity amounted to 1.1 billion dollars annually [8] . This underlines the substantial financial burden incurred due to the prevalence of obesity. Furthermore, obesity can lead to a range of health issues, either directly or indirectly. A longitudinal study conducted over a span of 14 years by the Framingham Heart Study revealed that individuals with obesity have","cbCaisS0hYOSscGS","https://ap.wps.com/l/cbCaisS0hYOSscGS","pdf",1651602,1,5,"English","en",105,"# Introduction\n## Obesity definitions and classification standards\n## Economic and health impacts of obesity\n## Motivation for lifestyle-based prediction\n## Machine learning modeling approach\n## Model comparison and key contributing factors","[{\"question\":\"What is the main goal of the study on obesity risk?\",\"answer\":\"To evaluate how well different machine learning models can forecast obesity levels using lifestyle habits, supporting targeted intervention for high-risk individuals.\"},{\"question\":\"Which dataset and labeling standard are used to build the obesity classification?\",\"answer\":\"The study uses the UCI Machine Learning Repository (2,111 subjects) and classifies obesity according to the Mexican Normativity aligned with CDC standards.\"},{\"question\":\"Why does the study exclude height and weight in its predictions?\",\"answer\":\"It focuses on lifestyle predictors, removing direct height and weight parameters to highlight behavioral factors that can guide personalized intervention.\"}]","Predicting Obesity Risk Through Lifestyle Habits - A Comparative Analysis of Machine Learning Models | PDF",1785813987,13,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-obesity-risk-through-lifestyle-habits-a-comparative-analysis-of-machine-learning-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"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/predicting-obesity-risk-through-lifestyle-habits-a-comparative-analysis-of-machine-learning-models/122975/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study on obesity risk?","Question",{"text":75,"@type":76},"To evaluate how well different machine learning models can forecast obesity levels using lifestyle habits, supporting targeted intervention for high-risk individuals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and labeling standard are used to build the obesity classification?",{"text":80,"@type":76},"The study uses the UCI Machine Learning Repository (2,111 subjects) and classifies obesity according to the Mexican Normativity aligned with CDC standards.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the study exclude height and weight in its predictions?",{"text":84,"@type":76},"It focuses on lifestyle predictors, removing direct height and weight parameters to highlight behavioral factors that can guide personalized intervention.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"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":21,"slug":137},19,"General","general"]