Document Type
Article
Publication Date
1-1-2021
Description
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Citation Information
Shatnawi, Maad; Shatnawi, Anas; AlShara, Zakarea; and Husari, Ghaith. 2021. Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis. International Journal of Advanced Computer Science and Applications. Vol.12(4). 444-452. https://doi.org/10.14569/IJACSA.2021.0120457 ISSN: 2158-107X
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