Interpretable Ensemble Models for Lifestyle-Based Sleep Disorder Prediction - Rahardian, Farhan; Rakasiwi, Sindhu (Journal of Applied Informatics and Computing, 2026)
Sleep disorders are a major global health concern that affect cognitive performance, mental well-being, and long-term physiological health. Conventional diagnostic methods such as polysomnography are time-consuming and resource-intensive, limiting their use for large-scale early detection. Therefore, machine learning offers a practical alternative for predictive and data-driven sleep disorder analysis. This study compares the performance of four ensemble learning algorithms Random Forest, Gradient Boosting, AdaBoost, and XGBoost in predicting sleep disorders based on lifestyle and physiological factors using the Sleep Health and Lifestyle dataset consisting of 374 samples and three classes: insomnia, none, and sleep apnea. The research workflow includes data preprocessing, feature encoding, dataset splitting (70:30), and hyperparameter optimization using Grid Search with 5-fold Cross Validation to improve model stability and generalization given the limited dataset size. Model evaluation is conducted using accuracy, precision, recall, and F1-score calculated with a macro-average approach to ensure fair multi-class performance assessment. The results show that AdaBoost and XGBoost achieve the highest test accuracy of 90.27%, while Random Forest and Gradient Boosting obtain 89.38%. Performance differences among models are relatively small (±1%) but indicate consistent predictive behavior. Feature importance analysis identifies BMI category and systolic blood pressure as the most influential predictors, followed by occupation and physical activity level, highlighting the relevance of lifestyle and physiological factors in sleep disorder risk. Overall, this study demonstrates that ensemble learning models provide reliable predictive performance and interpretable insights to support early detection of sleep disorders based on lifestyle patterns.