Is It Possible to Distinguish COVID-19 Cases and Influenza with Wearable Devices? Analysis with Machine Learning

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dc.contributor.authorSkibiƄska, Justynacs
dc.contributor.authorBurget, Radimcs
dc.coverage.issue3cs
dc.coverage.volume13cs
dc.date.accessioned2022-05-03T10:54:46Z
dc.date.available2022-05-03T10:54:46Z
dc.date.issued2022-04-28cs
dc.description.abstractThe COVID-19 situation is enforcing the creation of the diagnosis and supporting methods for early detection, which could serve as screening tools. In this paper, we introduced the methodologies based on wearable devices and machine learning, which distinguishes between COVID-19 disease and two types of Influenza. We checked the results of binary classification for various scenarios and multiclass classification. The results were evaluated separately for the cases before the pandemic and in the middle of the pandemic. In the middle of the pandemic, the best classification accuracy was achieved when distinguishing between COVID-19 and Influenza cases with k-NN (the balanced accuracy was equal to 73%). The highest sensitivity was achieved for Logistic Regression - 61%. The successful distinction between Influenza types was achieved in 80 % for XGBoost and Decision Tree. Additionally, the balanced accuracy for multiclass classification was equal to 69 % for k-NN.en
dc.formattextcs
dc.format.extent265-270cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationJournal of Advances in Information Technology. 2022, vol. 13, issue 3, p. 265-270.en
dc.identifier.doi10.12720/jait.13.3.265-270cs
dc.identifier.issn1798-2340cs
dc.identifier.other177691cs
dc.identifier.urihttp://hdl.handle.net/11012/204165
dc.language.isoencs
dc.publisherEngineering and Technology Publishingcs
dc.relation.ispartofJournal of Advances in Information Technologycs
dc.relation.urihttp://www.jait.us/index.php?m=content&c=index&a=show&catid=217&id=1225cs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1798-2340/cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectCOVID-19en
dc.subjectartificial intelligenceen
dc.subjectsignal processingen
dc.subjectmachine learningen
dc.subjectwearablesen
dc.titleIs It Possible to Distinguish COVID-19 Cases and Influenza with Wearable Devices? Analysis with Machine Learningen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-177691en
sync.item.dbtypeVAVen
sync.item.insts2023.01.19 12:52:32en
sync.item.modts2023.01.19 12:14:16en
thesis.grantorVysokĂ© učenĂ­ technickĂ© v Brně. Fakulta elektrotechniky a komunikačnĂ­ch technologiĂ­. Ústav telekomunikacĂ­cs
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