Document Type : Research Paper
Department of CSE, The NorthCap University, Gurugram, Haryana, India.
COVID-19, an epidemic disease, has challenged human lives all over the world. Governments and scientific communities are trying their level best to help the masses. This disease which is caused by corona virus majorly attacks the upper respiratory system rendering the human immunity incapacitated and, in some cases, proving fatal. Therefore, it is very much important to identify the infected people quickly and accurately, so that it can be prevented from spread. Early addressal of the symptoms can help to prevent the disease to become severe for all mankind. This calls for the development of a decision-making system to help the medical fraternity for the timely action. This proposed fuzzy based system predicts Covid-19 based on individuals’ symptoms and parameters. It receives input parameters as fever, cough, breathing difficulty, muscle ache, sore throat, travel history, age, medical history in the form of different membership functions and generates one output that predicts the likelihood of a person being infected with COVID-19 using Mamdani fuzzy inference system. The timely prognosis of the disease at home isolation or at the security checks can help the patient to seek the medical treatment as early as possible. Patient case studies, real time observations, cluster cases were studied to create the rule base for FDMS. The results are validated by using real-time individuals test cases on the proposed system which yields 97.2% accuracy, 100% sensitivity and 96.2% specificity.
- (2020). Coronavirus disease (COVID-2019) situation reports. Retrieved April 30, 2021 from https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/
- Agrebi, S., & Larbi, A. (2020). Use of artificial intelligence in infectious diseases. In Artificial intelligence in precision health(pp. 415-438). Academic Press.
- Ahaskar, A. (2020). How WhatsApp chatbots are helping in the fight against Covid-19. Retrieved March 27, 2021 from https://www.livemint.com/technology/tech-news/how-whatsapp-chatbots-are-helping-in-the-fight-against-covid-19-11585310168911.html
- Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE transactions on systems, man, and cybernetics, (1), 28-44. DOI: 1109/TSMC.1973.5408575
- Singh, H., Gupta, M. M., Meitzler, T., Hou, Z. G., Garg, K. K., Solo, A. M., & Zadeh, L. A. (2013). Real-life applications of fuzzy logic. Fuzzy Syst. https://doi.org/10.1155/2013/581879
- Ali, D., Yohanna, M., Ijasini, P. M., & Garkida, M. B. (2018). Application of fuzzy–Neuro to model weather parameter variability impacts on electrical load based on long-term forecasting. Alexandria engineering journal, 57(1), 223-233.
- Buriboev, A., Kang, H. K., Ko, M. C., Oh, R., Abduvaitov, A., & Jeon, H. S. (2019). Application of fuzzy logic for problems of evaluating states of a computing system. Applied sciences, 9(15), 3021. https://doi.org/10.3390/app9153021
- Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1), 1-13.
- Kunhimangalam, R., Ovallath, S., & Joseph, P. K. (2013). A novel fuzzy expert system for the identification of severity of carpal tunnel syndrome. BioMed research international. https://doi.org/10.1155/2013/846780
- Hassanzad, M., Orooji, A., Valinejadi, A., & Velayati, A. (2017). A fuzzy rule-based expert system for diagnosing cystic fibrosis. Electronic physician, 9(12), 5974. DOI:19082/5974
- Arji, G., Ahmadi, H., Nilashi, M., Rashid, T. A., Ahmed, O. H., Aljojo, N., & Zainol, A. (2019). Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification. Biocybernetics and biomedical engineering, 39(4), 937-955.
- Ahamad, M. K., & Bharti, A. K. (2021, March). Prevention from COVID-19 in India: Fuzzy Logic Approach. International conference on advance computing and innovative technologies in engineering (ICACITE)(pp. 421-426). IEEE.
- Shaban, W. M., Rabie, A. H., Saleh, A. I., & Abo-Elsoud, M. A. (2021). Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network. Applied soft computing, 99, 106906. https://doi.org/10.1016/j.asoc.2020.106906
- Sivanandam, S. N., Sumathi, S., & Deepa, S. N. (2007). Introduction to fuzzy logic using MATLAB(Vol. 1). Berlin: Springer.
-  Joint Mission. (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19) Retrieved from https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf
- Jin, Y. H., Cai, L., Cheng, Z. S., Cheng, H., Deng, T., Fan, Y. P., ... & Wang, X. H. (2020). A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version). Military medical research, 7(1), 1-23.
- Singhal, T. (2020). A review of coronavirus disease-2019 (COVID-19). The Indian journal of pediatrics, 87(4), 281-286.
- Rothe, C., Schunk, M., Sothmann, P., Bretzel, G., Froeschl, G., Wallrauch, C., ... & Hoelscher, M. (2020). Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. New England journal of medicine, 382(10), 970-971.
- Ji, L. N., Chao, S., Wang, Y. J., Li, X. J., Mu, X. D., Lin, M. G., & Jiang, R. M. (2020). Clinical features of pediatric patients with COVID-19: a report of two family cluster cases. World journal of pediatrics, 1. DOI: 1007/s12519-020-00356-2
- Andrews, M. A., Areekal, B., Rajesh, K. R., Krishnan, J., Suryakala, R., Krishnan, B., ... & Santhosh, P. V. (2020). First confirmed case of COVID-19 infection in India: A case report. The Indian journal of medical research, 151(5), 490. DOI: 4103/ijmr.IJMR_2131_20
-  Thangaraj, J. W. V., Murhekar, M., Mehta, Y., Kataria, S., Brijwal, M., Gupta, N., ... & Bhargava, B. (2020). A cluster of SARS-CoV-2 infection among Italian tourists visiting India, March 2020. The Indian journal of medical research, 151(5), 438. DOI: 4103/ijmr.IJMR_1722_20
- Adhikari, S. P., Meng, S., Wu, Y. J., Mao, Y. P., Ye, R. X., Wang, Q. Z., ... & Zhou, H. (2020). Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review. Infectious diseases of poverty, 9(1), 1-12.
- Rizvi, S., Mitchell, J., Razaque, A., Rizvi, M. R., & Williams, I. (2020). A fuzzy inference system (FIS) to evaluate the security readiness of cloud service providers. Journal of cloud computing, 9(1), 1-17.