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

Nur Azieta Mohamad Aseri, Mohd Arfian Ismail, Abdul Sahli Fakharudin, Ashraf Osman Ibrahim, Shahreen Kasim, Noor Hidayah Zakaria, Tole Sutikno

Abstract


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.

Keywords


COVID-19; Differential evolution; Fuzzy logic; Genetic algorithm; Meta-heuristic; Particle swarm optimization; TLBO Algorithm

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DOI: http://doi.org/10.11591/ijai.v11.i1.pp50-64

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