AI-DRIVEN PCOS PREDICTION USING A NEUROENDOCRINE STRESS INDEX AND WEARABLE INSPIRED FEATURES

Abstract

Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder in women of reproductive age, yet early risk stratification remains challenging due to its multifactorial nature. This paper proposes an AI-driven wearable framework for early PCOS prediction built around a novel Neuroendocrine Stress Index (NSI) — a composite biomarker aggregating pulse rate, thyroid stimulating hormone, prolactin, random blood sugar, and BMI — which captures the cumulative physiological stress burden associated with PCOS onset. The framework integrates clinical, hormonal, metabolic, and wearable-derived signals (heart rate, sleep duration, physical activity, stress level) from the Kaggle PCOS dataset, processed through a structured pipeline of median imputation, standardization, and feature selection. An ensemble of Random Forest, Logistic Regression, and XGBoost classifiers is trained and evaluated, achieving up to 83.4% accuracy. SHAP-based explainability identifies follicle count, AMH, BMI, and the NSI as the most influential predictors, enhancing clinical transparency. Results confirm that embedding stress-related wearable signals alongside conventional clinical features improves prediction accuracy and supports personalized, interpretable healthcare decision-making for women at risk of PCOS.