Development and validation of an explainable machine learning and nomogram model for early detection and risk stratification of polycystic ovary syndrome: a multicenter study - Yao, Bihua; Yu, Xingyu; Zhang, Yunyan; Chen, Jiayan; Zhu, Xiaotong; Zhang, Cheng; Jijun, Tong (Frontiers in Endocrinology, 2025)
BackgroundPolycystic ovary syndrome (PCOS) is a common endocrine–metabolic condition in reproductive-aged women, linked to infertility and long-term cardiometabolic risk. Early identification remains challenging because current diagnosis relies on hormone testing and imaging. This research sought to develop and evaluate an interpretable machine learning (ML) model and a simplified nomogram for the early detection of PCOS.MethodsData from 1,600 women at the First People’s Hospital of Jiashan were used for model training, with 283 external cases from Jiaxing Hospital of Traditional Chinese Medicine for validation. Twenty-three routine laboratory indicators were analyzed. After LASSO feature selection, seven ML algorithms were compared. The best-performing XGBoost model was interpreted using Shapley Additive exPlanations (SHAP). A logistic regression–based nomogram was developed from the key predictors.ResultsThe XGBoost model showed excellent discrimination (AUC = 0.919 internal; 0.923 external). SHAP identified DHEAS, AMH, TG, and age as key contributors. The nomogram also performed well (AUC = 0.901 train; 0.887 test).ConclusionsThis interpretable “XGBoost + SHAP” and nomogram framework provides an accurate, transparent, and practical tool for early PCOS screening and individualized management.