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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144388" target="_blank" >RIV/00216305:26220/22:PU144388 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.jait.us/index.php?m=content&c=index&a=show&catid=217&id=1225" target="_blank" >http://www.jait.us/index.php?m=content&c=index&a=show&catid=217&id=1225</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.12720/jait.13.3.265-270" target="_blank" >10.12720/jait.13.3.265-270</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/VI04000039" target="_blank" >VI04000039: Systém včasného záchytu infekce COVID-19 pro bezpečnost ohrožených skupin obyvatelstva s využitím umělé inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Advances in Information Technology

ISSN

1798-2340

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

AU - Austrálie

Počet stran výsledku

6

Strana od-do

265-270

Kód UT WoS článku

000884923300008

EID výsledku v databázi Scopus

2-s2.0-85130704741