Volume 14 - Journal of Control, Vol. 14, No. 5, Special Issue on COVID-19                   JoC 2021, 14 - Journal of Control, Vol. 14, No. 5, Special Issue on COVID-19: 49-57 | Back to browse issues page

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Kamarzarrin M, eghbal N. Modeling of self-assessment system of COVID-19 disease diagnosis using Type-2 Sugeno fuzzy inference system. JoC 2021; 14 (S1) :49-57
URL: http://joc.kntu.ac.ir/article-1-817-en.html
1- Sadjad University of technology
Abstract:   (3825 Views)
Due to the continuation of the pandemic of Coronavirus in the whole world, the number of deaths has reached over one million, based on the World Health Organization reports. Early diagnosis of the illness can be a great assistance in order to break the chain of disease transmission. Nowadays, COVID-19 test kits are so limited in numbers, and expensive in terms of a cost, which slows down the diagnosis procedure and makes it difficult, thus, it is necessary to diagnose the disease in the early stages, to prevent its incidence. Therefore, we decided to propose a self-assessment method for COVID-19 disease, using a type-2 Sugeno fuzzy inference system, which causes conservation in time and costs. The system is prepared based on 98 rules, according to the World Health Organization instructions, using MATLAB software to simulate and diagnose the disease. The results show that Sugeno fuzzy with better correlation coefficient R^2=0.94 and error squared RMSE = 0.045, respectively, has acceptable accuracy for estimating and identifying COVID-19 disease. The self-assessment consequences are very promising and can prevent the further spread of the disease.
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Type of Article: Research paper | Subject: COVID-19
Received: 2020/12/29 | Accepted: 2021/02/13 | Published: 2021/02/28

1. Shchelkanov, M. Y., Popova, A. Y., Dedkov, V. G., Akimkin, V. G., & Maleyev, V. V. (2020). History of investigation and current classification of coronaviruses (Nidovirales: Coronaviridae). Russian Journal of Infection and Immunity, 10(2) , 221-246. [DOI:10.15789/2220-7619-HOI-1412]
2. Song, Z., Xu, Y., Bao, L., Zhang, L., Yu, P., Qu, Y., ... & Qin, C. (2019). From SARS to MERS, thrusting coronaviruses into the spotlight. Viruses, 11(1) , 59. [DOI:10.3390/v11010059]
3. Adly, A. S., Adly, A. S., & Adly, M. S. (2020). Approaches based on artificial intelligence and the internet of intelligent things to prevent the spread of COVID-19: Scoping review. Journal of Medical Internet Research, 22(8) , e19104. [DOI:10.2196/19104]
4. Long, C., Xu, H., Shen, Q., Zhang, X., Fan, B., Wang, C. & Li, H. (2020). Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT. European journal of radiology, 108961. [DOI:10.1016/j.ejrad.2020.108961]
5. Larsen, J. R., Martin, M. R., Martin, J. D., Kuhn, P., & Hicks, J. B. (2020). Modeling the Onset of Symptoms of COVID-19. Frontiers in public health, 8, 473. [DOI:10.3389/fpubh.2020.00473]
6. Herrera-Viedma, E. (2015). Fuzzy sets and fuzzy logic in multi-criteria decision making. The 50th anniversary of Prof. Lotfi Zadeh's theory: Introduction. Technological and Economic Development of Economy, 21(5), 677-683. [DOI:10.3846/20294913.2015.1084956]
7. Kasabov, N. K., & Song, Q. (2002). DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE transactions on Fuzzy Systems, 10(2) , 144-154. [DOI:10.1109/91.995117]
8. Pham, B. H., Ha, H. T., & Ngo, L. T. (2012, December). Learning rule for TSK fuzzy logic systems using interval type-2 fuzzy subtractive clustering. In Asia-Pacific Conference on Simulated Evolution and Learning (pp. 430-439). Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-34859-4_43]
9. Verity, R., Okell, L. C., Dorigatti, I., Winskill, P., Whittaker, C., Imai, N., & Dighe, A. (2020). Estimates of the severity of coronavirus disease 2019: a model-based analysis. The Lancet infectious diseases. [DOI:10.1016/S1473-3099(20)30243-7]
10. Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685. [DOI:10.1109/21.256541]

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