Machine Learning-Driven Metabolomic Biomarker Discovery for PCOS: An Interpretable Approach Using Random Forest and SHAP.
Aim: This study aimed to predict Polycystic Ovary Syndrome (PCOS) using follicular fluid metabolomic data and the Random Forest algorithm, and to interpret the contributions of the most influential metabolites using SHapley Additive exPlanations (SHAP) analysis. Material and Method: An untargeted me...
| Publicado en: | Medical Records Vol. 7; no. 3; pp. 763 - 768 |
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| Autor principal: | |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Medical Records
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |