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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146274" target="_blank" >RIV/00216305:26220/22:PU146274 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9943460" target="_blank" >https://ieeexplore.ieee.org/document/9943460</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICUMT57764.2022.9943460" target="_blank" >10.1109/ICUMT57764.2022.9943460</a>

Result language
angličtina
Original language name
Wearable Analytics and Early Diagnostic of COVID-19 Based on Two Cohorts
Original language description
The outbreak of the COVID-19 pandemic forced a need to create screening tests to diagnose the disease. To answer this challenge, this paper introduces the support methodology for COVID-19 early detection based on wearable and machine learning likewise on two various cohorts. We compare the level of detection of the COVID-19 disease, Influenza, and Healthy Control (HC) thanks to the usage of machine learning classifiers likewise changes in heart rate and daily activity. The features obtained as the parameters of the ratio of heart rate to the variable of the number of steps proved to have the highest statistical importance. The COVID-19 cases versus HC were possible to be distinguished with 0.73 accuracy by the XGBoost algorithm, whereas COVID-19 cases, Influenza vs. HC were able to be differentiated on similar level of accuracy: in 0.72 by Support Vector Machine. The multiclass classification between the cases achieved a 0.57 F1-score for three classes by XGBoost. For early diagnosis, this solution could serve as an extra test for clinicians during the pandemic, and the result shows which metric could be useful for creating the machine learning model.
Czech name
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Czech description
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Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2022 14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshop (ICUMT)
ISBN
979-8-3503-9866-3
ISSN
2157-023X
e-ISSN
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Number of pages
8
Pages from-to
56-63
Publisher name
IEEE
Place of publication
Valencia, Spain
Event location
Valencia, Spain
Event date
Oct 11, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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