Development and Validation of a Disability Risk Prediction Model for Older Adults Based on Machine Learning: A Multi-Algorithm Comparison with SHAP Interpretation.

OBJECTIVE: To develop a predictive model for disability risk in older adults using machine learning algorithms. METHODS: A convenience sample of 13,809 older adults (aged ≥60 years) was recruited from seven medical institutions, three communities, and five nursing homes in Zunyi City, Guizhou Province. Participants were randomly divided into a training set (n = 9667) and a validation set (n = 4142) at a 7:3 ratio. Disability status was used as the outcome variable. Nine machine learning algorithms-logistic regression, decision tree, random forest, XGBoost, LightGBM, support vector machine, artificial neural network, K‑nearest neighbor, and naïve Bayes-were used to construct prediction models. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, and other metrics, and the best‑performing model was selected. The SHapley Additive exPlanations (SHAP) method was used for interpretability analysis of the optimal model. RESULTS: Among the 13,809 participants, 5308 (38.44%) were identified as having disability. Among the nine models, LightGBM achieved the highest AUC (0.859), accuracy (0.792), precision (0.771), sensitivity (0.651), specificity (0.880), and F1 score (0.706). CONCLUSION: Among the developed prediction models, the LightGBM‑based model demonstrated superior overall predictive performance in internal validation, providing a reference for disability management in older adults.