Predicting functional dependency using machine learning among a middle-aged and older Chinese population

Abstract

Objective: To develop prediction models for assessing functional dependency in a middle-aged and older Chinese population.

Method: Adults ≥45 years old from the China Health and Retirement Longitudinal Study (CHARLS) and without functional dependency at baseline were included. Functional dependency was defined as needing any help in any basic activities of daily living (ADL) or instrumental activities of daily living (IADL). The outcomes were overall functional dependency, ADL and IADL dependency. Stacked ensemble models were constructed based on five selected machine learning models. Models were trained and tested in the 2011-2015 cohort, and were externally validated in the 2015-2018 cohort. SHapley Additive exPlanations (SHAP) was utilized to quantify the significance of predictors.

Result: In the training cohort, a total of 6,297 participants were included at baseline, 1,893 developed functional dependency during the follow-up period. The stacked ensemble model achieved the best performance in terms of discrimination ability for predicting overall functional dependency, ADL and IADL dependency, with AUCs of 0.750, 0.690 and 0.748, respectively; in external validation cohort, the corresponding AUCs were 0.725, 0.719 and 0.727, respectively. A compact model was further developed and maintained similar predictive performance.

Conclusion: The stacked ensemble approach can serve as a useful tool for identifying the risk of functional dependency in a large Chinese population. For ADL dependency, arthritis, age, self-report health, and waist circumference were identified as highly significant predictors. Conversely, cognitive function, age, living in rural areas, and performance in chair stand test emerged as highly ranked predictors for IADL dependency.

Keywords: Cohort study; Ensemble learning; Functional dependency; Machine learning; Prediction model.