SAS Mobile Application for Diagnosis of Obstructive Sleep Apnea Utilizing Machine Learning Models - Haberfeld, Carl; Sheta, Alaa; Hossain, Md Shafaeat; Turabieh, Hamza; Surani, Salim (2020 11th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON), 2020)
In this paper, we provide a consistent, inexpensive, and easy to use graphical user interface (GUI) smart phone application named Sleep Apnea Screener (SAS) that can diagnosis Obstructive Sleep Apnea (OSA) based on demographic data such as: gender, age, height, BMI, neck circumference, waist, etc., allowing a tentative diagnosis of OSA without the need for overnight tests. The developed smart phone application can diagnosis sleep apnea using a model trained with 620 samples collected from a sleep center in Corpus Christi, TX. Two machine learning classifiers (i.e., Logistic Regression (LR) and Support Vector Machine (SVM)) were used to diagnosis OSA. Our preliminary results show that at-home OSA screening is indeed possible, and that our application is effective method for covering large numbers of undiagnosed cases.
Keywords: COVID-19; Corpus christi; Demographic data; Graphical user interfaces; Graphical user interfaces (GUI); Learning systems; Logistic regression; Logistics; Machine Learning; Machine learning; Machine learning models; Mathematical model; Medical services; Mobile applications; Mobile telecommunication systems; OSA; Obstructive sleep apnea; SVM; Sleep Apnea; Sleep apnea; Sleep research; Smart phones; Smart-phone applications; Smartphones; Support vector machines; Support vector regression; Two machines; Ubiquitous computing; dept.csc; pretesting