Understanding cross-data dynamics of individual and social/environmental factors through a public health lens: explainable machine learning approaches - Jeong, Siwoo; Yun, Sung Bum; Park, Soon Yong; Mun, Sungchul (Frontiers in Public Health, 2023)
The rising prevalence of obesity has become a public health concern, requiring efficient and comprehensive prevention strategies. This study innovatively investigates the combined influence of individual and social/environmental factors on obesity within the urban landscape of Seoul, employing advanced machine learning approaches. We collected 'Community Health Surveys' (n = 114,789) and credit card usage data (n = 1,512,232) to represent individual factors. In parallel, we utilized 'Seoul Open Data', which comprises green park (n = 131), bus (n = 10,985), subway (n = 645), land price (n = 909,496), bicycle (n = 12,462,884), and health budget (n = 75) datasets, 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 between the factors and the obesity rate in each Gu district into Seoul, taking into account spatial relationship. The 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. Our 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.