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DOI: 10.14569/IJACSA.2021.0120457
PDF

Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis

Author 1: Maad Shatnawi
Author 2: Anas Shatnawi
Author 3: Zakarea AlShara
Author 4: Ghaith Husari

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 4, 2021.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The coronavirus (COVID-19) pandemic has caused severe adverse effects on the human life and the global economy affecting all communities and individuals due to its rapid spreading, increase in the number of affected cases and creating severe health issues and death cases worldwide. Since no particular treatment has been acknowledged so far for this disease, prompt detection of COVID-19 is essential to control and halt its chain. In this paper, we introduce an intelligent fuzzy inference system for the primary diagnosis of COVID-19. The system infers the likelihood level of COVID-19 infection based on the symptoms that appear on the patient. This proposed inference system can assist physicians in identifying the disease and help individuals to perform self-diagnosis on their own cases.

Keywords: COVID-19; coronavirus diagnosis; fuzzy inference system; fuzzy logic; fuzzy rules; expert systems

Maad Shatnawi, Anas Shatnawi, Zakarea AlShara and Ghaith Husari, “Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis” International Journal of Advanced Computer Science and Applications(IJACSA), 12(4), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120457

@article{Shatnawi2021,
title = {Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120457},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120457},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {4},
author = {Maad Shatnawi and Anas Shatnawi and Zakarea AlShara and Ghaith Husari}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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