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

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. A...

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Bibliographic Details
Main Authors: Emek Guldogan, Fatma Hilal Yagin, Hasan Ucuzal, Sarah A. Alzakari, Amel Ali Alhussan, Luca Paolo Ardigò
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Medicina
Subjects:
breast cancer
metabolomics
explainable AI
LightGBM
SHAP
biomarkers
Online Access:https://www.mdpi.com/1648-9144/61/6/1112
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