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This research presents a novel detection model for Polycystic Ovary Syndrome (PCOS) that combines data balancing, feature selection, and deep learning techniques to improve diagnostic accuracy. The proposed model integrates SMOTE-ENN for dataset balancing, GS2TM for optimal feature selection, and DASCD for robust classification, achieving a state-of-the-art accuracy of 98.7%. The unified approach addresses key challenges in PCOS detection, such as data imbalance and overfitting, while enhancing interpretabi

Author

Tahmid tonmoy

Language

EN