A Multi‐Technique Feature Selection Framework Using Computational Intelligence and Internet of Medical Things for Early Detection of Polycystic Ovary Syndrome in Women

This study presents a comparative analysis of three feature selection methods, namely chi‐square test, backward elimination, and mutual information gain, for identifying the most significant features from a dataset of 541 patients with and without PCOS, and demonstrates the potential of ML and feature selection techniques in effectively detecting PCOS.