Machine learning estimates biological age from clinical data with clear explanations
Clinically Interpretable Biological Age Estimation Using CatBoost and SHAP with Continuous and Categorical Features from NHANES
Machine Learning
Summary
Biological age reflects how your body ages differently from your actual age. This paper shows how a computer model uses many health measurements to estimate biological age more clearly than before. The authors created an automated way to explain which health factors most affect the estimated age and by how much. Their method helps make complex machine learning results easier to understand for doctors and health professionals.
What this means in practice
- •For health data analysts: Use a combined clinical dataset and CatBoost with automated SHAP analysis to estimate and explain biological age from patient data.
- •For clinical decision support teams: Incorporate interpretable biological age estimates into patient assessments to identify key health factors influencing aging.
Authors
Yanni Sun, Zheng Qu, Kevin Ren, Zhanglin Lin Cui
Abstract
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 urea nitrogen, serum glucose, vigorous recreational activity, red cell distribution width, and alanine aminotransferase among the leading predictors, with three categorical variables ranking among the top 25. Automated quantitative SHAP analysis mapped predictor-specific ranges, effect sizes, and inflection points, while waterfall plots enabled individual explanations. By extending qualitative visual interpretation to reproducible quantitative metrics across heterogeneous clinical data, this framework enhances transparency and provides a scalable methodological foundation for biological aging research and precision health.