利用 NHANES 的连续型和分类型特征，基于 CatBoost 和 SHAP 进行具有临床可解释性的生物年龄估计

arXiv cs.LG 论文
biological-age shap catboost interpretability healthcare-ml nhanes precision-health

摘要

该论文提出一个临床可解释的机器学习框架，使用CatBoost和自动化的SHAP依赖图定量分析，从NHANES的44个异构临床特征中估计生物年龄。研究发现模型估计的生物年龄与实际年龄高度相关，并识别出血尿素氮、血糖和剧烈运动等关键预测因子。

arXiv:2610.20868v1 Announce Type: new 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.
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# Clinically Interpretable Biological Age Estimation Using CatBoost and SHAP with Continuous and Categorical Features from NHANES
Source: [https://arxiv.org/abs/2610.20868](https://arxiv.org/abs/2610.20868)
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> 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\.

## Submission history

From: Zheng Qu \[[view email](https://arxiv.org/show-email/8bf23a83/2610.20868)\] **\[v1\]**Wed, 14 Oct 2026 19:58:47 UTC \(1,718 KB\)

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