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This study presents a hybrid machine learning architecture aimed at improving the diagnostic accuracy of Polycystic Ovary Syndrome (PCOS), achieving a state-of-the-art accuracy of 96.88%. The approach integrates feature selection, data balancing using SMOTE, and explainable AI techniques (SHAP and LIME) to enhance model interpretability and reliability. A user-friendly Flask web application has been developed for real-time diagnostics, addressing the pressing need for accessible and automated PCOS identific

Author

Tahmid tonmoy

Language

EN