Machine Learning-Driven Polycystic Ovary Syndrome Detection with Feature Selection

PCOS is a common disorder that has a substantial negative impact on women's reproductive health and may even cause infertility. The goal of this research is to develop a machine learning model for PCOS early detection. PCOS is characterized by hormonal imbalances, which can manifest in diverse clinical presentations and elevate the risk of health complications, including type 2 diabetes. Despite its high prevalence and associated health risks, PCOS often goes unnoticed, with many individuals unaware of its symptoms. This study's primary objective is to increase public awareness of PCOS while providing a valuable resource for early detection. To accomplish this goal, we conducted a comprehensive analysis of relevant clinical data and employed advanced machine learning techniques such as Logistic Regression, Multi-Layer Perceptron (MLP) Classifier, Random Forest, Support Vector Classifier (SVM), and K-Nearest Neighbors (KNN) to construct a highly accurate predictive model. Our solution not only delivers precise predictions but also has the potential to revolutionize early PCOS diagnosis. This research paper marks a significant milestone in enhancing the understanding and management of PCOS, ultimately improving the quality of healthcare for women grappling with this condition.