Optimized Machine Learning Algorithms for the Classification and Diagnosis of Sleep Disorders

Sleep disorders, including insomnia and obstructive sleep apnea, affect millions of individuals worldwide but are frequently undetected due to the high cost, limited availability, and complexity of conventional diagnostic tools such as polysomnography. This study presents an interpretable machine learning framework for multi-class sleep disorder classification that utilizes routine clinical and lifestyle data, offering an accessible, data-driven approach to screening. The dataset was preprocessed and balanced using SMOTE-ENN, and three ensemble models, Random Forest, XGBoost, and LightGBM, were systematically trained and optimized. Among these, LightGBM demonstrated superior performance, achieving a test accuracy, precision, and recall of 0.9835, with a minimal train-test accuracy gap of 0.0105, indicating strong generalization and limited overfitting. Feature importance analysis identified systolic blood pressure and BMI category as the most influential predictors, consistent with established clinical knowledge. To enable practical application, the optimized model was deployed through a lightweight Flask-based web application, providing real-time, point-of-care predictions without requiring specialized equipment. The findings demonstrate that well-tuned tree-based ensemble models can deliver accurate, interpretable, and clinically actionable tools for the early detection of sleep disorders, supporting timely intervention and expanding access to sleep health management across diverse healthcare settings.