A review of fuzzy logic analysis in COVID-19 pandemic and a new technique through extended hexagonal intuitionistic fuzzy number in analysis of COVID-19

Abstract

Several intuitionistic fuzzy logic approaches have been used for the diagnosis of COVID-19 patients. We have developed a fuzzy rule base system for the detection of COVID-19 patients. In this study, we have considered six major parameters based symmetric/asymmetric, linear/non-linear hexagonal intuitionistic fuzzy numbers (HIFN) for the input-output factors of the problem. In real-life diagnosis problems, such as assessing COVID-19 symptoms, applying symmetric and asymmetric, linear and non-linear hexagonal intuitionistic fuzzy numbers allows for a more accurate representation of patient conditions. Centre of area method is used for the defuzzied value of the hexagonal intuitionistic fuzzy parameters. HIFN are used because they provide a detailed representation of uncertainty, incorporating both membership and non-membership degrees through six parameters. This flexibility allows for nuanced modelling of real-world scenarios, such as medical diagnoses, where data often includes ambiguity. Then the HIFN approach is used for obtaining the compromising and superlative solution in the diagnostic process of COVID-19 patients. To figure out the adaptability of the proposed HIFN based technique, a comparative study is also introduced. The originality, limitations, future aspects and advantages of using this HIFN based technique is also discussed in this article.