Convergence Institute of Human Data Technology, Jeonju University, Jeonju, Republic of Korea.
Department of Sports Rehabilitation Medicine, Kyungil University, Gyeongsan, Republic of Korea.
Front Public Health. 2023 Oct 26;11:1257861. doi: 10.3389/fpubh.2023.1257861. eCollection 2023.
The rising prevalence of obesity has become a public health concern, requiring efficient and comprehensive prevention strategies.
This 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.
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.
肥胖症的患病率不断上升，已成为公共卫生关注的焦点，需要制定高效、全面的预防策略。
本研究创新性地采用先进的机器学习方法，研究个体和社会/环境因素对首尔城市景观中肥胖的综合影响。我们收集了“社区健康调查”和信用卡使用数据来代表个体因素。同时，我们利用“首尔开放数据”来概括导致肥胖的社会/环境因素。我们使用随机森林模型基于个体因素预测肥胖。该模型进一步采用 Shapley Additive Explanations (SHAP) 算法来确定每个因素在肥胖预测中的相对重要性。对于社会/环境因素，我们使用地理加权最小绝对收缩和选择算子 (GWLASSO) 来计算回归系数。
随机森林模型预测肥胖的准确率>90%。Shapley 揭示了每个区中与肥胖相关的个体因素的多样性影响，但“对肥胖的自我意识”、“体重控制经验”和“高血压经验”是所有区中排名前五的影响因素。GWLASSO 表明社会/环境因素的回归系数在不同地区存在差异。
我们的研究结果为设计有针对性的肥胖预防计划提供了有价值的见解，该计划将不同的个体和社会/环境因素整合到城市设计中，即使是在同一个城市。本研究增强了可解释机器学习在制定城市健康策略中的有效开发和应用。我们建议每个自治区在制定预算计划时考虑这些差异化的影响因素，以有效地解决肥胖问题。
