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

• Main Authors: Nur Azieta, Mohamad Aseri, Mohd Arfian, Ismail, Abdul Sahli, Fakharudin, Ashraf Osman, Ibrahim, Shahreen, Kasim, Noor Hidayah, Zakaria, Sutikno, Tole
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2022
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TJ Mechanical engineering and machinery
• Online Access: http://umpir.ump.edu.my/id/eprint/33572/1/Comparison%20of%20meta-heuristic%20algorithms%20for%20fuzzy%20modelling%20of%20covid-19%20illness%E2%80%99.pdf; http://umpir.ump.edu.my/id/eprint/33572/; https://doi.org/10.11591/ijai.v11.i1.pp50-64; https://doi.org/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 meta-heuristic 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.