Sleep Disorder Prediction using Advanced Machine Learning Techniques

Author(s)	Mrs. P. Sarala, Ch. Charitha, Ch. Kusuma Kala, A. Yaswanthi, Abdul Haq
Country	India
Abstract	Sleep disorders like insomnia, sleep apnea, and restless legs syndrome significantly impact global health, increasing risks of cardiovascular diseases, cognitive decline, and reduced quality of life. Traditional diagnostic methods, such as polysomnography (PSG), are costly, resource-intensive, and require clinical monitoring. This study proposes a machine learning-based predictive model as a non-invasive, cost-effective, and accurate alternative for sleep disorder detection. By integrating supervised learning, ensemble methods, and recurrent neural networks (RNNs), the model analyzes physiological signals, medical history, sleep patterns, and demographic data. Comparative analysis shows that deep learning models, particularly those handling temporal data, achieve superior predictive accuracy. Future work will focus on incorporating diverse data sources, validating larger datasets, and exploring real-time applications for home-based and clinical monitoring.
Keywords	prediction of sleep disorders, detection of sleep apnea, classification of insomnia, neural networks (RNN, LSTM),ensemble learning, and evaluation of sleep health.
Field	Computer > Data / Information
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-03-11
DOI	https://doi.org/10.71097/IJSAT.v16.i1.2182

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology