Volume 14, Issue 5 (Journal of Control, Vol. 14, No. 5, Special Issue on COVID-19 2021)                   JoC 2021, 14(5): 71-78 | Back to browse issues page

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dehghandar M. Diagnosis of COVID-19 disease by fuzzy expert system designed based on input-output. JoC. 2021; 14 (5) :71-78
URL: http://joc.kntu.ac.ir/article-1-833-en.html
1- Payame Noor University, Karaj, Iran
Abstract:   (556 Views)
Accurate prediction and diagnosis of COVID-19 disease is very important for everyone, especially for medical professionals. On the other hand, the use of fuzzy systems in medicine is increasing rapidly. In this study, a fuzzy system was designed using the information of 375 patients suspected of having COVID-19 disease who referred to Imam Khomeini (Tehran), Alborz (Karaj) and Kowsar(Karaj) hospitals. For this purpose, 300 people were considered to extract the rules and 75 people were considered as test data. Information on 12 important parameters of COVID-19 disease including fever, cough, headache, gastrointestinal symptoms, skin rash, sense of smell and taste, underlying disease, chest CT, blood oxygen level, lethargy, age, family history and severity of COVID-19 disease received. The fuzzy expert system was designed with 29 rules after reviewing the rules and removing similar and contradictory rules by using their degree calculation. In this system, by integrating some factors, finally 8 input variables and one output variable were considered that was used by product inference engine, singleton fuzzifier and center average defuzzifier. It was observed that the designed fuzzy expert system provides very good results, so that it detects 93% of Covid-19 disease with high accuracy and also the sensitivity of the system is more than 95% and the specificity of the designed system is more than 87%.
Full-Text [PDF 1145 kb]   (164 Downloads)    
Type of Article: Research paper | Subject: COVID-19
Received: 2021/01/19 | Accepted: 2021/02/13 | Published: 2021/02/28

References
1. Conzade, R.; Grant, R.; Malik, M. R.; Elkholy, A.; Elhakim, M. et al. (2018): Reported direct and indirect contact with dromedary camels among laboratory-confirmed MERS-CoV cases. Viruses, vol. 10, no. 8, pp. 425-433. [DOI:10.3390/v10080425]
2. Danesh F,Ghavidel S,(2020) : Coronavirus : Scientometrics of 50 years of Global Scientific Produntions , Iran J Med. Microbiol; Vol. 14, no. 1, pp.1 - 16. [DOI:10.30699/ijmm.14.1.1]
3. Dehghandar M, Khaloozadeh H, etal (2016) : Application Of Fuzzy Logic to determine the retentive causes Of pulse body the pulse parameters in Iranian Traditional Medicine, Journal of Multidisciplinary Engineering Science and Technology , vol .3 Issue 2.
4. Conzade, R.; Grant, R.; Malik, M. R.; Elkholy, A.; Elhakim, M. et al. (2018): Reported direct and indirect contact with dromedary camels among laboratory-confirmed MERS-CoV cases. Viruses, vol. 10, no. 8, pp. 425-433. [DOI:10.3390/v10080425]
5. Memish, Z. A.; Cotton, M.; Meyer, B.; Watson, S. J. et al. (2014): Human infection with MERS coronavirus after exposure to infected camels, Saudi Arabia, 2013. Emerging Infectious Diseases, vol. 20, no. 6, pp. 1012-1018. [DOI:10.3201/eid2006.140402]
6. Müller, M. A.; Corman, V. M.; Jores, J.; Meyar, B.; Younan, M. et al. (2014): MERS coronavirus neutralizing antibodies in camels, Eastern Africa, 1983-1997. Emerging Infectious Diseases, vol. 20, no. 12, pp. 2093-2099. [DOI:10.3201/eid2012.141026]
7. Wang W, Tang J, Wei F. Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan, China. J Med Virol. 2020. [DOI:10.1002/jmv.25689]
8. Zhao Y, Zhao Z, Wang Y, Zhou Y, Ma Y, Zuo W. Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov. BioRxiv. 2020. [DOI:10.1101/2020.01.26.919985]
9. Wrapp D, Wang N, Corbett KS, Goldsmith JA, Hsieh CL, Abiona O, Graham BS, McLellan JS. Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science. 2020 Feb 19. [DOI:10.1101/2020.02.11.944462]
10. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet. 2020; 395(10223):497-506. [DOI:10.1016/S0140-6736(20)30183-5]
11. Emery SL, Erdman DD, Bowen MD, Newton BR, Winchell JM, Meyer RF, et al. Real-time reverse transcription-polymerase chain reaction assay for SARS-associated coronavirus. Emerging infectious diseases. 2004; 10(2):311-6. [DOI:10.3201/eid1002.030759]
12. Gaunt ER, Hardie A, Claas EC, Simmonds P, Templeton KE. Epidemiology and clinical presentations of the four human coronaviruses 229E, HKU1, NL63, and OC43 detected over 3 years using a novel multiplex real-time PCR method. Journal of clinical microbiology. 2010; 48(8):2940-7.. [DOI:10.1128/JCM.00636-10]
13. Wu F ZS, Bin Y, Chen YM, Wang W, Song ZG, Hu Y, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020. [DOI:10.1038/s41586-020-2202-3]
14. Zhou P YX, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020.
15. Asrardel M, "Prediction of Combustion Dynamics in an Experimental Turbulent Swirl Stabilized Combustor with Secondary Fuel Injection", University of Tehran, 2015.
16. Wang Li-Xin (1996). A course in fuzzy systems and control: prentice Hall International,Inc.

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