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AI model estimates biological age using diverse clinical data and SHAP

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel machine-learning framework to estimate biological age using a CatBoost regression model and SHAP for interpretability. This model was trained on a large dataset from the US National Health and Nutrition Examination Survey, incorporating a diverse range of features including demographic, laboratory, physical examination, and lifestyle predictors. The framework introduces an automated procedure for quantifying feature contributions from SHAP dependence plots, moving beyond visual inspection to provide reproducible quantitative metrics. The model demonstrated a strong correlation between estimated biological age and chronological age, and population-level SHAP analysis identified key predictors such as blood urea nitrogen and serum glucose. AI

IMPACT Enhances transparency in biological aging research and provides a foundation for precision health applications.

RANK_REASON The cluster contains an academic paper detailing a new methodology for biological age estimation using machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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AI model estimates biological age using diverse clinical data and SHAP

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The cluster contains an academic paper detailing a new methodology for biological age estimation using machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yanni Sun, Zheng Qu, Kevin Ren, Zhanglin Lin Cui ·

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

    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…

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