Explainable ensemble-based machine learning model for polycystic ovary syndrome detection using hybrid feature selection

Abstract Polycystic Ovary Syndrome (PCOS) is a common endocrine condition that needs accurate diagnosis for effective management. It involves the presence of numerous immature follicles in the ovaries, which can interfere with healthy ovulation and lead to hormonal imbalances and other health issues. Consequently, it is essential to establish a PCOS detection system that is both precise and timely to lower complications. In the current literature, Machine Learning (ML) models have demonstrated their efficacy in detecting PCOS. However, the accurate and early detection of PCOS requires the precise identification of key features. This paper proposes a hybrid framework for PCOS prediction that combines ensemble learning and feature selection. The proposed methodology integrates Genetic Algorithm (GA), Mutual Information (MI), and Boruta feature selection techniques to identify the most informative clinical and hormonal features. In addition, to facilitate a comparative evaluation of prediction performance, a variety of base and ensemble classifiers were trained with selected features. The hybrid feature set improved diagnostic accuracy and generalizability across models, establishing a comprehensible and effective method for PCOS identification that is suitable for clinical decision support. Additionally, SHAP-based feature interpretation is performed to assess the contributions of each feature. The proposed method is evaluated on a publicly available PCOS dataset. It exhibits superior performance compared to several existing approaches, achieving an accuracy of over 94% on all different combinations of feature sets and XGBoost.