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...
Αποθηκεύτηκε σε:
| Κύριοι συγγραφείς: | , , , , , |
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| Μορφή: | Artigo |
| Γλώσσα: | Inglês |
| Έκδοση: |
MDPI AG
2025-06-01
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| Σειρά: | Medicina |
| Θέματα: | |
| Διαθέσιμο Online: | https://www.mdpi.com/1648-9144/61/6/1112 |
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