<![CDATA[Undiagnosed sleep disorders pose significant cardiovascular risks, necessitating accessible screening tools beyond invasive clinical procedures. This study aims to develop a robust diagnostic framework using the Sleep Health and Lifestyle Dataset. To address class imbalance and enhance predictive sensitivity, a Stacking Ensemble architecture integrating Random Forest, Gradient Boosting, CatBoost, and XGBoost is implemented, augmented by Pulse Pressure feature engineering and the Synthetic Minority Over-sampling Technique (SMOTE). The proposed model achieved a superior accuracy of 98.61% and a recall of 99.24%, significantly outperforming single classifiers. Feature analysis further identified heart rate and sleep duration as critical physiological determinants. These findings conclude that combining feature engineering with optimized ensemble learning offers a highly accurate diagnostic approach with rapid training convergence, providing a scalable pathway for early sleep disorder detection.]]>
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