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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Development and external validation of a machine learning model for three-tier disability risk stratification in
Yitong Mao1, Zhiting Guo1, Jiayi Wang1
1Nursing Department, The Second Affiliated Hospital of Zhejiang University School of Medicine (SAHZU), Hangzhou, Zhejiang, China.
Background:
Global aging has made disability among older adults a major public health challenge. Existing prediction models often lack external validation and actionable risk stratification. This study aims to develop and externally validate an explainable, three-tier machine learning framework for disability risk prediction and stratification among older adults.
Methods:
The training set and internal validation set were derived from the longitudinal development cohort of the China Health and Retirement Longitudinal Study (CHARLS, n = 3,599). An independent cross-sectional survey (n = 2,066) was used for external validation of the model. Individuals 65 years of age and older were included. Disability was defined by basic activities of daily living (BADL). 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.
Results:
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.
Discussion:
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.