Predicting Obesity Risk Through Lifestyle Habits: A Comparative Analysis of Machine Learning Models

Author:

Wang Xiaotian

Abstract

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 K-Nearest 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.

Publisher

EDP Sciences

Reference19 articles.

1. Obesity as a medical problem

2. World Health Organization, World Health Organization. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight, (2021).

3. What use is the BMI?

4. National Health Service, 2023, https://www.nhs.uk/conditions/obesity/.

5. Centers for Disease Control and Prevention, Centers for Disease Control and Prevention. 2022. https://www.cdc.gov/obesity/index.html.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3