A Two-Stage Hybrid Feature Selection and Ensemble Learning Framework With Explainable AI for Accurate PCOS Prediction

Polycystic ovary syndrome (PCOS) is the most prevalent endocrine ailment impacting women of reproductive age, distinguished by ongoing imbalances of hormones that lead to the growth of various ovarian cysts alongside other health issues. Infrequent or delayed menstrual periods, and also frequently excessive amounts of androgen, a male hormone, are characteristics of women with PCOS. Despite other consequences, this disease may lead to weight gain, undesirable body hair, type 2 diabetes, and gestational diabetes. The number of cases of PCOS has shockingly ascended in the past few years, according to statistics. But the clinical diagnosis procedure for PCOS in the real world becomes critical since the precision of interpretations greatly relies on the knowledge of the doctor. Therefore, a PCOS prediction model using artificial intelligence might be an appropriate supplement to the lengthy and error-prone diagnosis method.

To strictly prevent data leakage, the dataset underwent an 80/20 train-test split before applying SMOTE and two-stage feature selection exclusively to the training data. This framework combined Chi-Square filtering with Recursive Feature Elimination to identify the most pertinent features. A hybrid XGBoost (XGB) and Multi-Layer Perceptron (MLP) ensemble was selected from multiple machine learning models following rigorous evaluation using 10-fold stratified cross-validation. Finally, SHapley Additive exPlanations and Local Interpretable Model-Agnostic Explanations were integrated to significantly enhance overall model transparency.

The hybrid ensemble model (XGB + MLP) outperformed conventional single ML models, with the greatest classification accuracy of 96.33% among all evaluated models. The model's interpretability and performance were greatly enhanced by the 16 characteristics that were chosen.

Combining hybrid ensemble learning, two-stage feature selection, and XAI approaches provides a computationally efficient, dependable, and interpretable method for PCOS diagnosis. Practitioners may find this model to be a useful decision-support tool that improves the accuracy of diagnosis and lessens the need for human interpretation.