Publication detail

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

SKIBIŃSKA, J. HOŠEK, J. CHANNA, A.

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

COVID-19, AI, wearable, machine learning

Authors

SKIBIŃSKA, J.; HOŠEK, J.; CHANNA, A.

Released

18. 11. 2022

Publisher

IEEE

Location

Valencia, Spain

ISBN

979-8-3503-9866-3

Book

2022 14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshop (ICUMT)

ISBN

2157-023X

Periodical

International Congress on Ultra Modern Telecommunications and Control Systems and Workshops

State

unknown

Pages from

56

Pages to

63

Pages count

8

URL

https://ieeexplore.ieee.org/document/9943460

BibTex

@inproceedings{BUT180042,
  author="SKIBIŃSKA, J. and HOŠEK, J. and CHANNA, A.",
  title="Wearable Analytics and Early Diagnostic of COVID-19 Based on Two Cohorts",
  booktitle="2022 14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshop (ICUMT)",
  year="2022",
  journal="International Congress on Ultra Modern Telecommunications and Control Systems and Workshops",
  pages="56--63",
  publisher="IEEE",
  address="Valencia, Spain",
  doi="10.1109/ICUMT57764.2022.9943460",
  isbn="979-8-3503-9866-3",
  issn="2157-023X",
  url="https://ieeexplore.ieee.org/document/9943460"
}