Interpretable machine learning for identifying overweight and obesity risk factors of older adults in China.

Abstract

Objective: To estimate the importance of risk factors on overweight/obesity among older adults by comparing different predictive model.

Methods: Survey data from 400 older individuals in China was employed to assess the impacts of four domains of risk factors (demographic, health status, physical activity and neighborhood environment) on overweight/obesity. Six machine learning algorithms were utilized for prediction, and SHapley Additive exPlanations (SHAP) was employed for model interpretation.

Results: The CatBoost model demonstrated the highest performance among the prediction models for overweight/obesity. Gender, transportation-related physical activity and road network density were top three important features. Other significant factors included falls, cardiovascular conditions, distance to the nearest bus stop and land use mixture.

Conclusion: Insufficient physical activity, denser road network and incidents of falls increased the likelihood of older adults being overweight/obese. Strategies for preventing overweight/obesity should target transportation-related physical activity, neighborhood environments, and fall prevention specifically.