Predicting the possibility of COVID-19 infection using fuzzy logic system
International Journal of Intelligent Information and Database Systems
; 14(3):239-256, 2021.
Article
in English
| Scopus | ID: covidwho-1346334
ABSTRACT
Diagnosing COVID-19 in a fast and efficient manner is an ongoing problem. Currently, the methods of detection involve physical tests. Physical tests have the disadvantage that they require either test kits or medical equipment. This paper outlines the design of a type-2 fuzzy logic system that can help provide a preliminary diagnosis by computing the possibility that a patient is suffering from COVID-19 based on their external symptoms. It uses input information that can be gleaned without need for medical procedures. Both statistical data and the knowledge base were garnered from publicly available databases and datasets. The fuzzy logic system implemented here is functional, but it is fairly inaccurate and illustrates that more information, both symptomatic and epidemiological is needed, to predict COVID-19 infections through an expert system. Copyright © 2021 Inderscience Enterprises Ltd.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
International Journal of Intelligent Information and Database Systems
Year:
2021
Document Type:
Article
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