Ümran Karabulut DOĞAN, Şeyma YAŞAR, Erhan Hüseyin CÖMERT, Erol KARAKAŞ, Ozan DOĞAN
Cyprus Journal of Medical Sciences - 2026;11(5):359-368
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. MATERIALS AND METHODS: A publicly available Kaggle dataset comprising 541 patients and 41 clinical, biochemical, and lifestyle features was utilised. After preprocessing-including median imputation and synthetic minority oversampling technique (SMOTE)-two ensemble classifiers were trained and validated using 5-fold stratified cross-validation, with SMOTE applied strictly within each training fold to prevent data leakage. Three baseline classifiers (logistic regression, random forest, and support vector machine) were included for comparison. SHapley Additive exPlanations (SHAP) summary plots, bar charts, waterfall diagrams, and local interpretable model-agnostic explanations (LIME) were employed for global and local model interpretability. RESULTS: XGBoost achieved an accuracy of 91.74%, an area under the curve-receiver operating characteristic (AUC-ROC) of 0.9522 [95% confidence interval (CI): 0.910-0.992], and an F1-score of 0.8696, whereas LightGBM attained an accuracy of 89.91%, an AUC-ROC of 0.9551 (95% CI: 0.914-0.993), and an F1-score of 0.8451. Corrected 5-fold CV AUC scores were 0.9701+/-0.0148 and 0.9682+/-0.0161, respectively. Both gradient boosting models outperformed all three baseline comparators. Calibration analysis yielded Brier scores of 0.071 and 0.079 for XGBoost and LightGBM, respectively, and decision curve analysis demonstrated positive net benefit over treat-all and treat-none strategies across clinically plausible threshold ranges. A sensitivity analysis excluding direct Rotterdam-criteria variables (follicle counts and cycle irregularity) yielded AUC-ROC values of 0.88 and 0.87, confirming residual discriminative capability of non-criterion features. SHAP analysis consistently identified right ovarian follicle number (Follicle No. R), hair growth (hirsutism), left ovarian follicle number (Follicle No. L), weight gain, and cycle irregularity as the five most influential predictors across both models. CONCLUSION: Both gradient boosting algorithms demonstrated high discriminative performance for PCOS classification within an internal validation framework. The integration of SHAP and LIME explanations provides clinically interpretable insights that are consistent with established PCOS diagnostic criteria. Because several of the most influential predictors (follicle counts and cycle irregularity) are themselves components of the diagnostic criteria, the models are best understood as automating the integration of established clinical features rather than discovering new ones. Accordingly, the pipeline is presented as a decision-support feasibility (proof-of-concept) framework rather than a tool ready for use in clinical practice. These findings represent a proof-of-concept for explainable gradient boosting pipelines in PCOS decision support; prospective external validation in independent, ethnically diverse cohorts is required before clinical deployment can be recommended.
