Interpretable machine learning models to predict decline in intrinsic capacity among older adults in China: a prospective cohort study.

Background: Monitoring intrinsic capacity and implementing appropriate interventions can support healthy aging. There are, though, few tools available for predicting decline in intrinsic capacity among older adults. This study aimed to develop and validate an interpretable machine learning model designed to identify populations at elevated risk of a decline in intrinsic capacity.

Methods: Using data from the China Health and Retirement Longitudinal Study baseline (2011) and 4-year follow-up (2015), a total of 822 participants were randomly allocated to a training set and a testing set at a 7:3 ratio. Five machine learning methods were employed to train the model and assess its performance through various metrics. The SHapley Additive exPlanation method was subsequently used to interpret the optimal model.

Results: The 4-year incidence of decline in intrinsic capacity among the older adults in the sample was 44.6 % (n = 367). Nine variables were screened for model construction, among which eXtreme gradient boosting demonstrated the best predictive performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.715 (95 % CI 0.651-0.780) in the testing set. The SHapley Additive exPlanation method identified educational level, smoking, handgrip strength, self-rated health, and residence as the top five significant predictors.

Conclusions: The developed model can serve as a highly effective tool for primary care teams to identify older adults with early signs of decline in intrinsic capacity, facilitating the provision of subsequent screening and tailored interventions for intrinsic capacity.