A fuzzy expert system for predicting the mortality of COVID’19

The COVID-19 pandemic has had a widespread impact on health and economy across the globe. It is leading to a huge number of deaths per day. Few researchers have been attracted to analyzing the mortality rate of COVID-19 from various perspectives. During the research, it has become evident that these fatalities are not only caused by COVID-19, but they are also affected by some other factors. The authors of this paper aim to encompass three important types of factors viz. risk factors, clinical factors, and miscellaneous factors that influence the mortality of COVID-19. This manuscript presents a rule-based model under the Mamdani-based fuzzy expert system (FES) to analyze the mortality rate of the highly contagious COVID-19. The proposed model creates three FESs and thereafter generates the final FES which aggregates these three FESs. The FES for risk value considers 5 aggregate factors viz. immunity, temperature, ventilation, population density, and pollution. The second FES is to model the clinical facilities based on ICU count, quarantine centers, and tests performed. The third FES is created to model the miscellaneous factors. Finally, the concluding FES combines three base FESs to evaluate the mortality value. The results obtained by the suggested model are promising and hence advocate the efficacy of the proposed model. [ABSTRACT FROM AUTHOR] Copyright of Turkish Journal of Electrical Engineering & Computer Sciences is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)