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arXiv Machine Learning By 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

Read the original on arXiv Machine Learning →

The study presents a clinically interpretable machine‑learning framework that estimates biological age (BA) using a CatBoost regression model trained on 44 diverse predictors from NHANES data. SHAP values are leveraged not only for visual interpretation but also through an automated quantitative procedure that identifies feature‑value ranges linked to the lowest and highest BA contributions, including zero‑crossing thresholds. The resulting BA estimates correlate strongly with chronological age and highlight key predictors such as blood urea nitrogen, serum glucose, and vigorous activity, offering transparent, reproducible insights for precision health research.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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