Machine learning-based optimization of pre-symptomatic COVID-19 detection through smartwatchopen access
- Cho, Hyeong Rae; Kim, Jin Hyun; Yoon, Hye Rin; Han, Yong Seop; Kang, Tae Seen; Choi, Hyunju; Lee, Seunghwan
- Issue Date
- NATURE PORTFOLIO
- SCIENTIFIC REPORTS, v.12, no.1
- Journal Title
- SCIENTIFIC REPORTS
- Patients with weak or no symptoms accelerate the spread of COVID-19 through various mutations and require more aggressive and active means of validating the COVID-19 infection. More than 30% of patients are reported as asymptomatic infection after the delta mutation spread in Korea. It means that there is a need for a means to more actively and accurately validate the infection of the epidemic via pre-symptomatic detection, besides confirming the infection via the symptoms. Mishara et al. (Nat Biomed Eng 4, 1208-1220, 2020) reported that physiological data collected from smartwatches could be an indicator to suspect COVID-19 infection. It shows that it is possible to identify an abnormal state suspected of COVID-19 by applying an anomaly detection method for the smartwatch's physiological data and identifying the subject's abnormal state to be observed. This paper proposes to apply the One Class-Support Vector Machine (OC-SVM) for pre-symptomatic COVID-19 detection. We show that OC-SVM can provide better performance than the Mahalanobis distance-based method used by Mishara et al. (Nat Biomed Eng 4, 1208-1220, 2020) in three aspects: earlier (23.5-40% earlier) and more detection (13.2-19.1% relative better) and fewer false positives. As a result, we could conclude that OC-SVM using Resting Heart Rate (RHR) with 350 and 300 moving average size is the most recommended technique for COVID-19 pre-symptomatic detection based on physiological data from the smartwatch.
- Files in This Item
- There are no files associated with this item.
- Appears in
- College of Medicine > Department of Medicine > Journal Articles
- 해양과학대학 > 지능형통신공학과 > Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.