cvd-ml-xai-risk-score

Reproducible machine learning pipeline for cardiovascular disease risk classification (n=68,630) comparing logistic regression and gradient-boosted models (XGBoost, LightGBM, Random Forest) with SHAP-based explainability, decision curve analysis, restricted cubic spline modeling, and subgroup validation.

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	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
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