Detail publikace

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

SKIBIŃSKA, J. BURGET, R.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

The 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.

Klíčová slova

COVID-19, artificial intelligence, signal processing, machine learning, wearables

Autoři

SKIBIŃSKA, J.; BURGET, R.

Vydáno

28. 4. 2022

Nakladatel

Engineering and Technology Publishing

ISSN

1798-2340

Periodikum

Journal of Advances in Information Technology

Ročník

13

Číslo

3

Stát

Australské společenství

Strany od

265

Strany do

270

Strany počet

6

URL

http://www.jait.us/index.php?m=content&c=index&a=show&catid=217&id=1225

Plný text v Digitální knihovně

http://hdl.handle.net/11012/204165

BibTex

@article{BUT177691,
  author="Justyna {Skibińska} and Radim {Burget}",
  title="Is It Possible to Distinguish COVID-19 Cases and Influenza with Wearable Devices? Analysis with Machine Learning",
  journal="Journal of Advances in Information Technology",
  year="2022",
  volume="13",
  number="3",
  pages="265--270",
  doi="10.12720/jait.13.3.265-270",
  issn="1798-2340",
  url="http://www.jait.us/index.php?m=content&c=index&a=show&catid=217&id=1225"
}