Improved composite relation for pythagorean fuzzy sets and its application to medical diagnosis

Abstract

Uncertainty is an important factor in any decision-making process. Different mathematical frameworks have been introduced to cope the ambiguity of decision-making. The concept of Pythagorean fuzzy sets (PFSs) is one of the latest mathematical frameworks that deals with uncertainty. Pythagorean fuzzy sets generalize intuitionistic fuzzy sets with a wider scope of applications, and hence, the motivation for investigating into its applicability in tackling uncertainty imbedded in medical diagnosis. This paper studies the approach of max–min–max composite relation for Pythagorean fuzzy sets, improves upon the approach, and applies its to medical diagnosis problem. The validity of the improved composite relation for Pythagorean fuzzy sets is carried out in comparison to the max–min–max composite relation for Pythagorean fuzzy sets using numerical experiments. The improved composite relation for Pythagorean fuzzy sets yields a better relation with a greater relational value when compared to the aforementioned composite relation and, hence, its choice to solving medical diagnosis problem. To this end, an application of the improved composite relation for Pythagorean fuzzy sets is explored in medical diagnosis using hypothetical medical database. This improved composite relation could be used as a sustainable approach in applying Pythagorean fuzzy sets to multi-criteria decision-making (MCDM) problems, multi-attribute decision-making (MADM) problems, pattern recognition problems, among others.