Prediction of polycystic ovary syndrome using machine learning with SFS and Boruta feature selection: an explainable AI approach

Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting numerous women of reproductive age, characterized by a variety of clinical and biochemical features. Accurate classification and diagnosis of PCOS remains challenging due to the heterogeneous nature of its manifestations. This study introduces a robust machine learning framework that combines a voting ensemble model with two distinct feature selection techniques, Sequential Forward Selection (SFS) and Boruta, to enhance the accuracy in classifying PCOS. We also utilized Explainable Artificial Intelligence (XAI) techniques, such as Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), Partial Dependence Plot (PDP), AnchorTabular, and Permutation Importance, to interpret the ensemble model. These methods provide essential insights into the significance of key features for predicting PCOS patients. Results show that the proposed ensemble learning model achieved optimal performance with the feature selection technique used. Specifically, the proposed voting ensemble classifier and features picked by SFS had the highest accuracy among all models. This method can help in PCOS diagnosis and support early intervention.