Feature-Driven Sleep Disorder Classification Using Machine Learning

Sleep disorders significantly affect an individual's physical health, mental well-being, and quality of life. Thus, early diagnosis of insomnia, sleep apnea, narcolepsy, and restless legs syndrome will help in preventing long-term hazards to health. State-of-the-art diagnostic methods suffer from great expenses, time-consuming processes, and highly specialized clinical infrastructure. For overcoming these challenges, here proposes a machine learning-based technique for the classification of sleep disorders that could be attributed easily to physiological and lifestyle attributes.The proposed system uses features such as age, sleep duration, quality of sleep, physical activity level, level of stress, heart rate, daily steps, and body mass index. Various supervised learning algorithms such as Support Vector Machine, k-Nearest Neighbors, Decision Tree, and Random Forest are implemented and analyzed. Feature scaling and model tuning techniques are used to improve the classification performance and reduce overfitting. The system will predict five output categories, namely insomnia, sleep apnea, narcolepsy, restless legs syndrome, and no sleep disorder by multiclass classification.