Understanding cross-data dynamics of individual and social/environmental factors through a public health lens: explainable machine learning approaches

Frontiers in Public Health

Public Health

Sungchul Mun¹  Siwoo Jeong²  Sung Bum Yun³  Soon Yong Park³ 

[1] Convergence Institute of Human Data Technology, Jeonju University, Jeonju, Republic of Korea;Department of Industrial Engineering, Jeonju University, Jeonju, Republic of Korea;Convergence Institute of Human Data Technology, Jeonju University, Jeonju, Republic of Korea;Department of Sports Rehabilitation Medicine, Kyungil University, Gyeongsan, Republic of Korea;Urban Strategy Research Division, Seoul Institute of Technology, Seoul, Republic of Korea;

关键词: obesity;    machine learning;    SHAP;    GWLASSO;    influential factors;   

DOI  :  10.3389/fpubh.2023.1257861

 received in 2023-08-16, accepted in 2023-10-09,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionThe rising prevalence of obesity has become a public health concern, requiring efficient and comprehensive prevention strategies.MethodsThis study innovatively investigated the combined influence of individual and social/environmental factors on obesity within the urban landscape of Seoul, by employing advanced machine learning approaches. We collected ‘Community Health Surveys’ and credit card usage data to represent individual factors. In parallel, we utilized ‘Seoul Open Data’ to encapsulate social/environmental factors contributing to obesity. A Random Forest model was used to predict obesity based on individual factors. The model was further subjected to Shapley Additive Explanations (SHAP) algorithms to determine each factor’s relative importance in obesity prediction. For social/environmental factors, we used the Geographically Weighted Least Absolute Shrinkage and Selection Operator (GWLASSO) to calculate the regression coefficients.ResultsThe Random Forest model predicted obesity with an accuracy of >90%. The SHAP revealed diverse influential individual obesity-related factors in each Gu district, although ‘self-awareness of obesity’, ‘weight control experience’, and ‘high blood pressure experience’ were among the top five influential factors across all Gu districts. The GWLASSO indicated variations in regression coefficients between social/environmental factors across different districts.ConclusionOur findings provide valuable insights for designing targeted obesity prevention programs that integrate different individual and social/environmental factors within the context of urban design, even within the same city. This study enhances the efficient development and application of explainable machine learning in devising urban health strategies. We recommend that each autonomous district consider these differential influential factors in designing their budget plans to tackle obesity effectively.

【 授权许可】

Unknown   
Copyright © 2023 Jeong, Yun, Park and Mun.