Table 1_Development and external validation of a machine learning model for three-tier disability risk stratification in older adults.docx

After feature selection, five candidate machine learning techniques were evaluated to identify the optimal model. The SHapley Additive exPlanations (SHAP) was used to interpret the optimal model. Ultimately, a three-tier risk stratification method was developed and verified using Decision Curve Analysis (DCA) and the Cochran-Armitage trend test.</p>Results<p>LASSO regression identified seven critical predictors.

The Extreme Gradient Boosting (XGBoost) achieved the optimal balance, achieving the optimal balance of predictive performance in the internal testing set (AUC = 0.737) and external validation cohort (AUC = 0.782). SHAP analysis revealed age, age-adjusted Charlson Comorbidity Index (ACCI), and lower limb mobility as the paramount global predictors. The established risk stratification thresholds (33.17 and 65.23%) successfully categorized older adults into three levels.

This three-tier system exhibited a significant gradient increase in actual disability incidence within the external set (P_trend < 0.001) and a broad positive net clinical benefit in DCA.</p>Discussion<p>The XGBoost model showed satisfactory and consistent performance in external validation. SHAP provided clinically meaningful insights, emphasizing age, lower-limb mobility, psychological status, and comorbidity burden.

The validated three-tier system bridges the translational gap between complex predicted probabilities and practical clinical decisions.</p>