Machine Learning-Based Optimization of Pre-Symptomatic COVID-19 Detection Through Smartwatch

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.[1] 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.[1] 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 RHR (Resting Heart Rate) 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.

Wearable Analytics and Early Diagnostic of COVID-19 Based on Two Cohorts

“…PCovNet recognised 100 % of the ill person based on RHR, nevertheless after recognised person as infected, so on quarantine. Additional approach of anomaly detection based on the dataset from [14] was provided in [17]. They applied One Class-Support Vector Machine (OC-SVM) and they achieved better results than in [14].…”