Research Article
Prediction of Sleep Apnea using Machine Learning
@INPROCEEDINGS{10.4108/eai.28-4-2025.2358055, author={Sulthan Alikhan and K Naveen and Golla Anand and K Pavan}, title={Prediction of Sleep Apnea using Machine Learning}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part II}, publisher={EAI}, proceedings_a={ICITSM PART II}, year={2025}, month={10}, keywords={sleep apnea apnea hypopnea index(ahi) machine learning}, doi={10.4108/eai.28-4-2025.2358055} }- Sulthan Alikhan
K Naveen
Golla Anand
K Pavan
Year: 2025
Prediction of Sleep Apnea using Machine Learning
ICITSM PART II
EAI
DOI: 10.4108/eai.28-4-2025.2358055
Abstract
This research presents an implementation of a web app that is designed and evaluated for non-invasive estimation of risk of sleep apnea. It employs machine learning-based algorithms to evaluate user-provided data (including demographic details, lifestyle information and answers to symptom-related questionnaires) to assess the likelihood of a person developing sleep apnea. The web interface input and output elements are user friendly, therefore, the tool is fun to use for non-experts. This paper investigates how effective several machine learning models, that are based on a rather relevant dataset, namely Random Forest, Logistic Regression and Support Vector Machines, are. We examine performance metrics accuracy, precision, recall and F1-score to select the optimal model to predict risk of sleep apnea. The aim of this web application is to serve as a preliminary diagnostic tool for users who might be at a greater risk and should seek a professional medical evaluation and diagnosis. This approach is intended to aid in early detection and treatment of sleep apnea.
