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...

وصف كامل

التفاصيل البيبلوغرافية
الحاوية / القاعدة:Medicina
المؤلفون الرئيسيون: Emek Guldogan, Fatma Hilal Yagin, Hasan Ucuzal, Sarah A. Alzakari, Amel Ali Alhussan, Luca Paolo Ardigò
التنسيق: مقال
اللغة:الإنجليزية
منشور في: MDPI AG 2025-06-01
الموضوعات:
breast cancer
metabolomics
explainable AI
LightGBM
SHAP
biomarkers
الوصول للمادة أونلاين:https://www.mdpi.com/1648-9144/61/6/1112

الانترنت

https://www.mdpi.com/1648-9144/61/6/1112

مواد مشابهة