Sleep Disorder Prediction using Machine Learning

Bibin Varghese, Sindhu Daniel

Abstract: The paper focuses on the classification of sleep disorders using machine learning algorithms (MLAs) to improve the diagnosis and monitoring of sleep health. Accurate classification of sleep disorders is critical for better patient care and quality of life. Traditionally, sleep-stage classification has been a challenging task prone to human error due to the complexity of analyzing sleep data. The development of machine learning models can automate this process, reducing errors and enhancing efficiency. This paper compares deep learning algorithms with conventional MLAs to assess their effectiveness in classifying sleep disorders. Using the publicly available Sleep Health and Lifestyle Dataset, which includes 400 rows and 13 features related to sleep and daily activities, different algorithms such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest are evaluated.

Keywords: Sleep Disorder, Machine Learning, KNN, SVM, Random Forest, Deep Learning, Sleep Health Dataset