Clinically Interpretable Biological Age Estimation Using CatBoost and SHAP with Continuous and Categorical Features from NHANES
Summary
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。
View Cached Full Text
Cached at: 10/16/26, 06:54 AM
# 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) [View PDF](https://arxiv.org/pdf/2610.20868) > 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 \(1,718 KB\)
Similar Articles
Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset
This study develops an XGBoost classifier using SHAP explainability on eight clinical biomarkers from the ADNI dataset to achieve three-class Alzheimer's disease detection (normal cognition, MCI, AD), reaching a macro AUC of 0.982 and Cohen's kappa of 0.909 on the held-out test set. SHAP analysis identifies CDR Global as the dominant predictor for NC and MCI, while CDR-SB and MMSE together drive AD classification.
Accelerometry-Derived Digital Biomarkers for Cardiometabolic Risk: A Population-Representative Tabular Benchmark with Uncertainty Quantification
This paper introduces the NHANES Accelerometry Cardiometabolic Benchmark, a population-representative tabular dataset for predicting cardiometabolic risk from accelerometry data, and evaluates ridge regression, XGBoost, and TabPFN v2 with uncertainty quantification using conformal prediction.
Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans
This paper presents a machine learning framework using CatBoost and SHAP to predict obstructive coronary artery disease from CT calcium scoring scans, achieving high accuracy by combining calcium-omics and epicardial fat features.
Population Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney Disease
The paper presents a two-part study using machine learning on large-scale telehealth data to classify self-reported chronic kidney disease status and identify key psychosocial and medical predictors, achieving balanced accuracy around 72-76% with SHAP analysis for interpretability.
Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms
This paper uses machine learning models trained on transcranial Doppler features to predict brain vascular age, finding accelerated aging in subjects with brain diseases such as stroke and Alzheimer's.