Behavioral and Lifestyle Determinants of Nutritional Status: A Comparative Machine Learning Analysis of BMI and WHR Among University Students

Behavioral and Lifestyle Determinants of Nutritional Status: A Comparative Machine Learning Analysis of BMI and WHR Among University Students | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Behavioral and Lifestyle Determinants of Nutritional Status: A Comparative Machine Learning Analysis of BMI and WHR Among University Students Manh Toan Nguyen, Minh Nguyen Xuan, Minh Hai Bui, Duy Manh Le, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9318316/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract This study evaluates machine learning models for predicting nutritional status among university students using Body Mass Index (BMI) and Waist–Hip Ratio (WHR) as target variables. A dataset of 819 students was collected through structured surveys covering demographic, anthropometric, health, dietary, and lifestyle factors. Multiple classifiers, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), were assessed using Accuracy, weighted F1-score, and cross-validation. Results show that BMI is more predictable than WHR, with Random Forest achieving the highest Accuracy (0.7805) and strong generalization, while KNN obtains the best F1-score (0.6942). In contrast, WHR yields lower performance across all models, with a maximum Accuracy of 0.6829, indicating weaker and less stable relationships with observed features. Feature importance analysis suggests that BMI is influenced by a wide range of behavioral and lifestyle factors, whereas WHR depends on fewer but less consistent predictors. These findings highlight structural differences between the two indicators and provide guidance for model selection and feature design in survey-based nutritional assessment. BMI classification WHR prediction Machine learning Student health Lifestyle factor Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 19 May, 2026 Reviews received at journal 18 May, 2026 Reviews received at journal 18 May, 2026 Reviews received at journal 15 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviews received at journal 13 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 05 May, 2026 Editor invited by journal 13 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 12 Apr, 2026 First submitted to journal 12 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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