Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis
| Autor: | Anas Shatnawi, Maad Shatnawi, Zakarea Alshara, Ghaith Husari |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
General Computer Science Coronavirus disease 2019 (COVID-19) Computer science Human life 02 engineering and technology Disease computer.software_genre Fuzzy logic Expert system 030218 nuclear medicine & medical imaging The primary diagnosis 03 medical and health sciences 0302 clinical medicine Fuzzy inference system Pandemic 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Intensive care medicine computer |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 12 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2021.0120457 |
| Popis: | 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 © 2021 |
| Databáze: | OpenAIRE |
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