Clinically Interpretable Biological Age Estimation Using CatBoost and SHAP with Continuous and Categorical Features from NHANES

Yanni Sun, Zheng Qu, Kevin Ren, Zhanglin Lin Cui

Biological age (BA) captures inter-individual variation in physiological aging beyond chronological age (CA). Although machine-learning models have advanced BA estimation, many rely primarily on continuous laboratory biomarkers, and SHapley Additive exPlanations (SHAP) dependence plots are typically interpreted visually. We developed a clinically interpretable machine-learning framework for BA estimation using heterogeneous clinical data from the National Health and Nutrition Examination Survey (NHANES 2011-March 2020, N = 24,983 adults aged >= 18). A CatBoost regression model incorporated 44 demographic, laboratory, physical examination, and lifestyle predictors to estimate CA, defining model-estimated age as BA. SHAP provided multi-level interpretability. Crucially, building on standard visual inspection, we implemented a prespecified automated procedure directly on SHAP dependence plots to quantify feature-value ranges associated with lowest and highest BA contributions, corresponding adjustment magnitudes, and zero-crossing thresholds. Model-estimated BA strongly correlated with CA in held-out testing data (Pearson r = 0.798, P < 0.001). Population-level SHAP identified blood…