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

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.