Machine learning-based optimization of pre-symptomatic COVID-19 detection through smartwatch

Hyeong Rae Cho; Jin Hyun Kim; Hye Rin Yoon; Yong Seop Han; Tae Seen Kang; Hyunju Choi; Seunghwan Lee

doi:10.1038/s41598-022-11329-y

May 12, 2022Scientific ReportsOpen Access

Machine learning-based optimization of pre-symptomatic COVID-19 detection through smartwatch

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Why the study?

Patients with weak or no symptoms accelerate the spread of COVID-19, driving the need to more actively and accurately detect infection pre-symptomatically using smartwatch physiological data.

Does One Class-Support Vector Machine (OC-SVM) improve pre-symptomatic COVID-19 detection using smartwatch physiological data compared to a Mahalanobis distance-based method?

Structured PICO

Does One Class-Support Vector Machine (OC-SVM) improve pre-symptomatic COVID-19 detection using smartwatch physiological data compared to a Mahalanobis distance-based method?

P

Population

Individuals with smartwatch physiological data for pre-symptomatic COVID-19 detection

I

Intervention

One Class-Support Vector Machine (OC-SVM) anomaly detection method using Resting Heart Rate (RHR)

C

Comparator

Mahalanobis distance-based method

O

Outcome

Pre-symptomatic COVID-19 detection performance (earlier detection, more detection, fewer false positives)surrogate

OC-SVM using resting heart rate from smartwatches improves the early and accurate pre-symptomatic detection of COVID-19 compared to previous distance-based methods.

Implication

Emphasizes need for active pre-symptomatic validation in COVID-19; leaves open optimal detection strategies.

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.