Interpretable Machine Learning for Serum-Based Metabolomics in Breast Cancer Diagnostics: Insights from Multi-Objective Feature Selection-Driven LightGBM-SHAP Models

<i>Background and Objectives:</i> Breast cancer accounts for 12.5% of all new cancer cases in women worldwide. Early detection significantly improves survival rates, but traditional biomarkers like CA 15-3 and HER2 lack sensitivity and specificity, particularly for early-stage disease. Advances in m...

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Auteurs principaux:	Emek Guldogan, Fatma Hilal Yagin, Hasan Ucuzal, Sarah A. Alzakari, Amel Ali Alhussan, Luca Paolo Ardigò
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2025-06-01
Collection:	Medicina
Sujets:	breast cancer metabolomics explainable AI LightGBM SHAP biomarkers
Accès en ligne:	https://www.mdpi.com/1648-9144/61/6/1112
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