Enhancing PCOS Prediction Using Machine Learning and Explainable AI

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder among women of reproductive age, requiring timely diagnosis and intervention to manage associated health risks. This research explores the application of various machine learning (ML) techniques for detecting PCOS, providing a comparative analysis of nine advanced methodologies: Extreme Learning Machine (ELM), Isolation Forest, Factorization Machines (FM), Multivariate Gaussian Process (MGP), Non-negative Matrix Factorization (NMF), Gaussian Processes (GP), Deep Belief Networks (DBN), Particle Swarm Optimization (PSO), and Long Short-Term Memory (LSTM). The integration of Explainable AI techniques enhances the interpretability of these models, offering greater transparency in the decision-making process. The study leverages a dataset containing PCOS-related data to validate the proposed approach, evaluating the strengths and limitations of each ML model in terms of accuracy, interpretability, and diagnostic capability. This research aims to bridge the gap between advanced computational techniques and clinical applications, contributing to the growing field of ML-driven healthcare diagnostics. The findings highlight the potential of combining diverse machine learning frameworks to improve early detection and personalized treatment strategies for PCOS, ultimately improving patient outcomes.