SAS Mobile Application for Diagnosis of Obstructive Sleep Apnea Utilizing Machine Learning Models
Resource type
Authors/contributors
- Haberfeld, Carl (Author)
- Sheta, Alaa (Author)
- Hossain, Md Shafaeat (Author)
- Turabieh, Hamza (Author)
- Surani, Salim (Author)
Title
SAS Mobile Application for Diagnosis of Obstructive Sleep Apnea Utilizing Machine Learning Models
Abstract
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.
Proceedings Title
2020 11th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON)
Conference Name
2020 11th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON)
Date
2020-10
Pages
0522-0529
Citation Key
haberfeldSASMobileApplication2020
Language
English
Library Catalog
IEEE Xplore
Extra
7 citations (Crossref) [2023-10-31]
Citation
Haberfeld, C., Sheta, A., Hossain, M. S., Turabieh, H., & Surani, S. (2020). SAS Mobile Application for Diagnosis of Obstructive Sleep Apnea Utilizing Machine Learning Models. 2020 11th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON), 0522–0529. https://doi.org/10.1109/uemcon51285.2020.9298041
Department
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