Polycystic Ovary Syndrome Detection Using Gradient Boosting Algorithms and Explainable Artificial Intelligence: A Comparative Study

Background/aims

Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders affecting women of reproductive age, with a global prevalence ranging from 6% to 21%. Early and accurate diagnosis is critical to preventing long-term metabolic, reproductive, and cardiovascular complications. This study aims to develop and compare two gradient-boosting machine-learning, extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), for PCOS classification using clinical and hormonal features and to enhance model interpretability through explainable artificial intelligence techniques.