Publication detail

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

SKIBIŃSKA, J. BURGET, R.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

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

Authors

SKIBIŃSKA, J.; BURGET, R.

Released

28. 4. 2022

Publisher

Engineering and Technology Publishing

ISBN

1798-2340

Periodical

Journal of Advances in Information Technology

Year of study

13

Number

3

State

Australia

Pages from

265

Pages to

270

Pages count

6

URL

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

Full text in the Digital Library

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"
}