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Prognostic Model for the Risk of Coronavirus Disease (COVID-19) Using Fuzzy Logic Modeling

Prognostic Model for the Risk of Coronavirus Disease (COVID-19) Using Fuzzy Logic Modeling

Florence Alaba Oladeji, Jeremiah Ademola Balogun, Temilade Aderounmu, Theresa Olubukola Omodunbi, Peter Adebayo Idowu
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 14
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.299378
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MLA

Oladeji, Florence Alaba, et al. "Prognostic Model for the Risk of Coronavirus Disease (COVID-19) Using Fuzzy Logic Modeling." JITR vol.15, no.1 2022: pp.1-14. http://doi.org/10.4018/JITR.299378

APA

Oladeji, F. A., Balogun, J. A., Aderounmu, T., Omodunbi, T. O., & Idowu, P. A. (2022). Prognostic Model for the Risk of Coronavirus Disease (COVID-19) Using Fuzzy Logic Modeling. Journal of Information Technology Research (JITR), 15(1), 1-14. http://doi.org/10.4018/JITR.299378

Chicago

Oladeji, Florence Alaba, et al. "Prognostic Model for the Risk of Coronavirus Disease (COVID-19) Using Fuzzy Logic Modeling," Journal of Information Technology Research (JITR) 15, no.1: 1-14. http://doi.org/10.4018/JITR.299378

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Abstract

This study formulated a model for assessing the risk of coronavirus disease (COVID-19) based on variables associated with the spread of COVID-19 infections. The study used the Mamdani fuzzy logic model based on a multiple input and single output (MISO) scheme which required 12 inputs and 1 output variable. Each of the input variables was identified using binary values, namely: No and Yes while the spread of COVID-19 was assessed using four nominal linguistic values. Two triangular membership functions were used to formulate each associated variable and four triangular membership functions to formulate the spread of COVID-19 using specific crisp intervals. The results of the study showed that 4096 rules were inferred from the possible combination of the binary linguistic values of the associated variables for the assessment of the spread of COVID-19. The study concluded that knowledge about variables associated with the spread of COVID-19 infection can be adopted for supporting decision-making which affects the assessment of the spread of COVID-19 by stakeholders.