Interpreting PCOS Risk Factors Using Explainable AI and Ensemble Modeling Techniques

Polycystic Ovary Syndrome (PCOS) is a common endocrine illness that affects women worldwide and can cause serious problems like infertility and an increased risk of miscarriage. It is often caused by high androgen and male hormone levels. This hormonal disorder disrupts the menstrual cycle, leading to irregular, delayed, or absent periods. PCOS can also contribute to severe health complications, including diabetes, gestational diabetes, weight gain, and excessive body hair. Artificial Intelligence (AI) has significantly transformed healthcare, enabling advancements in both science and engineering domains. To identify the most relevant features for PCOS detection Recursive Feature Elimination (RFE) is used, and several ensemble models such as bagging, boosting, voting classifiers, and stacking are compared. Among these, the Random Forest method using bagging achieved the highest accuracy. To enhance interpretability and assist medical professionals in decision-making, Explainable AI (XAI) techniques such as SHAP (SHapley Additive exPlanations) are applied to the Random Forest model that offers valuable insights into the factors influencing PCOS.