Sleep Disorder Detection Using Machine Learning

Abstract: Sleep disorders significantlyimpactqualityof life and overall health. This paperpresentsanensemble machine learning approach for accurate sleep disorder classification using demographic and health-related features. We implement and compare five models: Decision Tree, Logistic Regression, XGBoost, Support Vector Machine (SVM), and a Stacking Classifier combining SVM and XGBoost. The dataset undergoes comprehensive preprocessing including label encoding, SMOTE oversampling for class imbalance, and feature standardization. Experimental results demonstrate that XGBoost achieves the highest accuracy (94.95%), followed by the Stacking Classifier (93.43%), SVM (91.92%), Logistic Regression (90.40%), and Decision Tree (87.37%). The proposed approach showssignificant promise for clinical decisionsupportsystems in sleep medicine.