PCAUSE: A Web-Based Machine Learning Tool for PCOS Pre-Assessment Using Noninvasive Features

Abstract

Polycystic Ovary Syndrome (PCOS) remains a highly prevalent yet underdiagnosed endocrine disorder affecting women of reproductive age. This study proposes PCAUSE, a web-based, machine learning-powered pre-assessment tool designed to evaluate PCOS risk using only noninvasive clinical and lifestyle features. Through rigorous preprocessing—including data cleaning, multicollinearity reduction, and class imbalance correction via BorderlineSMOTE—this research examines the predictive performance of nine individual classifiers and two ensemble techniques across four methodological configurations. Mutual Information Feature Selection (MIFS) was employed to retain the most informative features, and hyperparameter tuning via GridSearchCV optimized model performance. Among all configurations, K-Nearest Neighbors (KNN), enhanced with both BorderlineSMOTE and MIFS, emerged as the most effective classifier, achieving the highest sensitivity (0.86), crucial for early detection and reducing false negatives. The final deployed system integrates this best-performing model and incorporates LIME for local explainability, offering transparent, actionable insights. Positioned as a clinically supportive and user-friendly screening tool, PCAUSE bridges the diagnostic gap by empowering women and aiding healthcare providers in early risk identification—particularly in resource-constrained environments.