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.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etsu-works-10998 |
| Date | 01 January 2021 |
| Creators | Shatnawi, Maad, Shatnawi, Anas, AlShara, Zakarea, Husari, Ghaith |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | ETSU Faculty Works |
| Rights | http://creativecommons.org/licenses/by/4.0/ |
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