Predicting the possibility of COVID-19 infection using fuzzy logic system. (3rd May 2021)

• Record Type: Journal Article
• Authors: Choudhury, Shadab Hafiz; Aurin, Azmary Jannat; Mitaly, Tanbin Akter; Rahman, Rashedur M.
• Is Part Of: International journal of intelligent information and database systems. Volume 14:Number 3(2021)
• Page Start: 239
• Page End: 256
• Publication Date: 2021-05-03
• Subjects: iterative type-2 fuzzy logic system -- Mamdani fuzzy inference -- novel coronavirus -- COVID-19; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

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.