PREDICTING POLYCYSTIC OVARY SYNDROME (PCOS) USING MACHINE LEARNING: A COMPREHENSIVE APPROACH TO DATA INTEGRATION, FEATURE ENGINEERING, AND MODEL OPTIMIZATION

This study presents a comprehensive approach to predicting PCOS using machine learning techniques, focusing on data integration, feature engineering, and model optimization, and demonstrates that the optimized Random Forest model achieved the highest predictive accuracy and robustness.