Cross-disciplinary risk prediction for muscle weakness and physical decline in older adults: A machine learning model integrating social determinants of health and clinical characteristics.

BackgroundWith the aging population, muscle weakness and physical decline are pressing public health concerns. Health outcomes are influenced by both physiological factors and social determinants of health; however, the interplay between these remains underexplored.ObjectiveThis study aimed to identify risk factors for muscle weakness and physical decline in older adults, integrating physiological and social determinants of health variables, and develop a predictive model for early risk assessment.MethodsUsing prospective China Health and Retirement Longitudinal Study data with 9-year follow-up data (baseline predictors from 2011, outcomes from 2020), logistic regression, recursive feature elimination, and XGBoost algorithms were applied to construct predictive models.ResultsKey risk factors included age, ethnicity, cognitive function, and physical activity. Social determinants of health variables such as marital status, life satisfaction, and educational level were significant predictors. SHapley Additive exPlanations analysis revealed that social determinants of health variables significantly enhanced model performance and interpretability.ConclusionIntegrating social determinants of health with clinical indicators improves the prediction of muscle weakness and physical decline in older adults. The study highlights the need for personalized interventions that consider both physiological and social factors, offering valuable insights for public health policy and health management in the aging population.