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

ABSTRACT Polycystic ovary syndrome (PCOS) is a prevalent endocrine condition in women of reproductive age that is linked to a number of issues with metabolism and reproductive health. For PCOS to be effectively treated and managed, a precise and early diagnosis is essential. In recent years, machine learning (ML) algorithms have demonstrated promising results in the diagnosis of PCOS by extracting pertinent features from clinical and laboratory data. Motivated by the diagnostic complexity and subjectivity in current PCOS identification techniques, this study addresses the gap in systematic evaluations of feature selection and classifier performance. The key objective is to develop an effective ML‐based diagnostic framework by identifying the most informative features, selecting optimal classifiers, and analyzing the impact of data balancing and validation strategies. In this study, we present 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. Additionally, we investigate the impact of dataset resampling on classification performance, evaluate the classification performance of individual features, compare the performance of various classifiers, and assess the classification performance for different values of k in k‐fold cross‐validation. As evidenced by our findings, the backward feature elimination method performs better than the other two methods in terms of identifying pertinent features for PCOS diagnosis, obtaining a Random Forest (RF) classifier accuracy of 96.55%. The most important features identified were anti‐Mullerian hormone (AMH) level, regularity or irregularity in cycle, follicle no. in left and right ovaries, and endometrium dimension. Our results illustrate the significance of taking into account various feature selection methods to uncover the most relevant features and demonstrate the potential of ML and feature selection techniques in effectively detecting PCOS. Additionally, we briefly explore the potential integration of real‐time data from Internet of Medical Things (IoMT) devices into the PCOS diagnosis framework to enhance its real‐world applicability.