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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ashraf Osman Ibrahim
Alzaiem Alazhari University
Computer Science & IT Engineering

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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
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp50-64

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.
Co-Authors Abdul Sahli Fakharudin Mohd Arfian Ismail Noor Hidayah Zakaria Nur Azieta Mohamad Aseri Shahreen Kasim Tole Sutikno