Enhancing COVID-19 Diagnosis through Fuzzy Logic Framework-A Comprehensive Approach

Nidhi Mishra

Authors

Nidhi Mishra Department of Mathematics, Pt. Harishankar Shukala Smriti Mahavidyalaya, Raipur, Chhattisgarh, India

Keywords:

Fuzzy Logic, Artificial intelligence, Expert system, Medical diagnosis, COVID-19

Abstract

COVID-19 is caused by a dangerous novel coronavirus known as severe acute respiratory syndrome corona-virus 2, first identified in the City of Wuhan in China. Since then, it has been declared a global pandemic by the World Health Organization. The late diagnosis of COVID-19 patients caused the fast spreading of the virus worldwide. This paper discusses how fuzzy logic and a rule-based expert system can help diagnose or detect COVID–19 in the early stages and get the result immediately without any delay.

References

The Star Online. (2020, December 24). Covid-19: Patient 26's higher infection rate could be due to virus mutation. Retrieved from [Link]

World Health Organization. (2020, December 23). Coronavirus. Retrieved from [Link]

Ministry of Health and Family Welfare, Government of India. (2020). Detail Question and Answers on COVID-19 for Public. Retrieved from https://www.mohfw.gov.in/pdf/FAQ.pdf

Ibrahim, F., Basheer, A. J., Jaais, F., &Taib, M. (2020). Expert system for early diagnosis of eye diseases infecting the Malaysian population. IEEE Catalogue No. 01 CH37239, 3(2), 123.

Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8, 338-353.

Faran, B., Khan, M. S., Noor, Y., & Imran, M. (2011). Design model of fuzzy logic medical diagnosis control system. International Journal on Computer Science and Engineering (IJCSE), 3(5), 2093-2108.

Darestani, A. Y., &Jahromi, A. E. (2009). Measuring customer satisfaction using a fuzzy inference system. Journal of Applied Sciences, 9(3), 469-478.

Engelbrecht, A. P. (2002). Computational intelligence: An introduction. John Wiley & Sons.

Obot, O. U., &Uzoka, F.-M. E. (2008). Experimental study of fuzzy rule-based management of tropical diseases: Case of malaria diagnosis. Medical and Care Compunetics, 5, 328–339.

Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37, 77-84.

World Health Organization. (2021). The symptoms of COVID19. Retrieved from https://www.who.int/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-a-detail/coronavirus-disease-covid-19#:126:text=symptoms.

Ejodamen, P. U., &Ekong, V. E. (2021). A fuzzy expert system modal for the determination of coronavirus risk. International Journal of Mechatronics, Electrical and Computer Technology (IJMEC), 11(39), 4825-4831.

Jadhav, B. T., &Nhivekar, G. S. (2021). Fuzzy expert system for severity measurement of COVID-19 suspect. International Journal of Research and Review, 8(12).

Shatnawi, M., AlShara, Z., Shatnawi, A., &Husari, G. (2021). Symptoms-based fuzzy-logic approach for COVID-19 diagnosis. International Journal of Advanced Computer Science and Applications, 12(4).

Sathyapriya, S., Priyadharshini, M., Bharani, M., Selvapriya, K., &Jesintha, S. (2021). Analysis of Covid 19 on fuzzy logic and decision making using Matlab. NVEO, 8(5), 4858-4870.

Painuli, D., Mishra, D., Bhardwaj, S., &Aggarwal, M. (2020). Fuzzy rule-based system to predict COVID19 - A deadly virus. International Journal of Management and Humanities (IJMH), 4(8).

Simsek, H., &Yangin, E. (Year). An alternative approach to determination of Covid-19 personal risk index by using fuzzy logic. Springer Series in Biomaterials Science and Engineering, Health and Technology, 12, 569-582.

Patel, A., Choubey, J., Gupta, S. K., Verma, M. K., Prasad, R., &Rahman, Q. et al. (2012). Decision support system for the diagnosis of asthma severity using fuzzy logic. In Proceedings of International Multiconference of Engineers and Computer Scientists (Vol. 1).

Tiwari, M., & Mishra, B. (2011). Application of cluster analysis in expert system - A brief survey. Journal Name, 8, 342-346.

Kumar, A. V. S., et al. (2013). Diagnosis of heart disease using advanced fuzzy resolution mechanism. International Journal of Science and Applied Information Technology (IJSAIT), 2(2), 22-30.

Onuwa, O. B., et al. (2014). Fuzzy expert system for malaria diagnosis. International Open Free Access, Peer Reviewed Research Journal, 7(2), 273-284.

Malathi, A., &Santra, A. K. et al. (Year). Diagnosis of lung cancer disease using neuro-fuzzy logic. CARE Journal of Applied Research.

Saritas, I., Allahverdi, N., &Sert, I. U. et al. (2003). A fuzzy expert system design for diagnosis of prostate cancer. In International Conference on Computer Systems and Technologies – CompSysTech.

Balanică, V., Dumitrache, I., Caramihai, M., Rae, W., Herbst, C. et al. (2011). Evaluation of breast cancer risk by using fuzzy logic. U.P.B. Sci. Bull., Series C, 73(1).

Zarei, K. H., Kamyad, A. V., &Heydari, A. A. et al. (2012). Fuzzy modeling and control of HIV infection. Computational and Mathematical Methods in Medicine, 2012.

Rao, S. G., Rao, M. E., & Prasad, D. S. (2013). Fever diagnosis rule-based expert systems. International Journal of Engineering Research & Technology (IJERT), 2(8).

Sharma, A. K. (2020a). A Brief Overview of Fuzzy Logic Theory. International Journal of Multidisciplinary Educational Research, 9(2(2)), 74–84.

http://s3-ap-southeast-1.amazonaws.com/ijmer/pdf/volume9/volume9-issue2(2)-2020.pdf

Sharma, A. K. (2020b). The Applicability of Concepts of Fuzzy Set Theory. International Journal of Research in Engineering, Science and Management, 3(1), 201–206.

https://www.ijresm.com/Vol.3_2020/Vol3_Iss1_January20/IJRESM_V3_I1_46.pdf

Animesh Kumar Sharma, B.V.Padamwar, & C L Dewangan. (2013). Trends in Fuzzy Graphs. International Journal of Innovative Research in Science, Engineering and Technology, 2(9), 4636–4640.

Sharma, A. K. (2012). Application of Fuzzy Logic in Decision Making Systems. Abhinav Journal of Research in Science & Technology, 1(12), 1–6.

Sharma, A. K. (2013). Fuzzy Logic for Utilization in Intelligence Cycle and in Generation of Alternatives. Abhinav Journal of Reasearch in Science & Technology, 2(1), 1–4.

Sharma, A. K. (2019). Overview of Some Applications of Fuzzy Generated Systems. International Journal of Innovative Research in Technology, 6(6), 12–15. https://ijirt.org/master/publishedpaper/IJIRT148746_PAPER.pdf