Guldogan, E.; Yagin, F.H.; Ucuzal, H.; Alzakari, S.A.; Alhussan, A.A.; Ardigò, L.P.
Interpretable Machine Learning for Serum-Based Metabolomics in Breast Cancer Diagnostics: Insights from Multi-Objective Feature Selection-Driven LightGBM-SHAP Models. Medicina 2025, 61, 1112.
https://doi.org/10.3390/medicina61061112
AMA Style
Guldogan E, Yagin FH, Ucuzal H, Alzakari SA, Alhussan AA, Ardigò LP.
Interpretable Machine Learning for Serum-Based Metabolomics in Breast Cancer Diagnostics: Insights from Multi-Objective Feature Selection-Driven LightGBM-SHAP Models. Medicina. 2025; 61(6):1112.
https://doi.org/10.3390/medicina61061112
Chicago/Turabian Style
Guldogan, Emek, Fatma Hilal Yagin, Hasan Ucuzal, Sarah A. Alzakari, Amel Ali Alhussan, and Luca Paolo Ardigò.
2025. "Interpretable Machine Learning for Serum-Based Metabolomics in Breast Cancer Diagnostics: Insights from Multi-Objective Feature Selection-Driven LightGBM-SHAP Models" Medicina 61, no. 6: 1112.
https://doi.org/10.3390/medicina61061112
APA Style
Guldogan, E., Yagin, F. H., Ucuzal, H., Alzakari, S. A., Alhussan, A. A., & Ardigò, L. P.
(2025). Interpretable Machine Learning for Serum-Based Metabolomics in Breast Cancer Diagnostics: Insights from Multi-Objective Feature Selection-Driven LightGBM-SHAP Models. Medicina, 61(6), 1112.
https://doi.org/10.3390/medicina61061112
