Polycystic Ovary Syndrome (PCOS) is the most prevalent endocrine disorder affecting women of reproductive age, and there is a need for early and accurate diagnosis to prevent long-term reproductive and metabolic consequences. This work introduces a transparent ensemble model to predict PCOS at the individual level using everyday clinical, hormonal, metabolic, and lifestyle parameters. This work allows patient-based prediction, as this includes biologically plausible predictors such as serum testosterone, the luteinizing hormone to follicle-stimulating hormone ratio (LH/FSH), insulin, and menstrual irregularities. A scheme for data preprocessing, feature selection, model training, and testing is proposed. The performance of four classical classifiers, i.e., Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (GB), is evaluated. Then, a voting-based ensemble method is proposed to enhance robustness and generalization. Results of experiments confirm good predictive performance, even for a significantly challenging task, with 96% accuracy and an ROC–AUC of 0.99, while decreasing false-negative rates is highly important for early screening. To add a layer of transparency and clinical trustworthiness, SHAP-based explainable artificial intelligence was adopted to evaluate global and patient-level feature importance. In addition to binary prediction, the proposed model reduces risk stratification to a probabilistic scale (low, moderate, high), making it more practical for clinical decision support. In conclusion, our proposed explainable ensemble framework presents a scalable, accurate, and interpretable decision support system for PCOS that is feasible to adopt in practical healthcare environments, especially in rural areas where medical resources are limited and more medical aids for the detection of such diseases are desperately needed. It has strong potential for integration into AI-enabled clinical screening systems.
Pavate et al. (Mon,) studied this question.