Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

• Published in: IAES International Journal of Artificial Intelligence Vol. 11; no. 1; p. 50
• Main Authors: Mohamad Aseri, Nur Azieta, Ismail, Mohd Arfian, Fakharudin, Abdul Sahli, Ibrahim, Ashraf Osman, Kasim, Shahreen, Zakaria, Noor Hidayah, Sutikno, Tole
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.03.2022
• Subjects: Algorithms; Classification; Coronaviruses; COVID-19; Datasets; Heuristic; Heuristic methods; Parameter identification; Signs and symptoms; Viral diseases
• ISSN: 2089-4872; 2252-8938; 2089-4872
• DOI: 10.11591/ijai.v11.i1.pp50-64

The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.