Machine learning-based optimization of pre-symptomatic COVID-19 detection through smartwatch.
Sci Rep
; 12(1): 7886, 2022 05 12.
Article
em En
| MEDLINE
| ID: mdl-35550526
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
COVID-19
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
País/Região como assunto:
Asia
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Coréia do Sul