Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder-affecting women of reproductive age, characterized by hormonal imbalance, metabolic complications, and reproductive issues. Early diagnosis remains challenging due to heterogeneous symptoms and reliance on subjective clinical criteria. Recent advancements in Machine Learning (ML) have shown promising results in improving diagnostic accuracy; however, the lack of interpretability limits their adoption in clinical practice. This review paper presents a comprehensive analysis of ML-based PCOS prediction models with a focus on Explainable Artificial Intelligence (XAI). It explores the role of ML algorithms such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), and ensemble techniques in enhancing prediction performance. Additionally, the study highlights preprocessing methods including SMOTE, feature scaling, and feature selection techniques that improve model efficiency. This review provides valuable insights into the development of accurate, interpretable, and reliable PCOS diagnostic systems, bridging the gap between computational intelligence and clinical applicability.
Bhardwaj et al. (Thu,) studied this question.