A Matrix-based Distance of Pythagorean Fuzzy Set and its Application in Medical Diagnosis

TL;DR

This paper introduces a new matrix-based distance measure for Pythagorean fuzzy sets that improves accuracy and reliability in distinguishing sets, with practical application demonstrated in medical diagnosis.

Contribution

A novel distance measurement method for Pythagorean fuzzy sets is proposed, satisfying axioms and better capturing differences than existing methods.

Findings

The new method avoids counterintuitive results.

It outperforms existing methods in accuracy and efficiency.

Effective in real-world medical diagnosis applications.

Abstract

The pythagorean fuzzy set (PFS) which is developed based on intuitionistic fuzzy set, is more efficient in elaborating and disposing uncertainties in indeterminate situations, which is a very reason of that PFS is applied in various kinds of fields. How to measure the distance between two pythagorean fuzzy sets is still an open issue. Mnay kinds of methods have been proposed to present the of the question in former reaserches. However, not all of existing methods can accurately manifest differences among pythagorean fuzzy sets and satisfy the property of similarity. And some other kinds of methods neglect the relationship among three variables of pythagorean fuzzy set. To addrees the proplem, a new method of measuring distance is proposed which meets the requirements of axiom of distance measurement and is able to indicate the degree of distinction of PFSs well. Then some numerical examples are offered to to verify that the method of measuring distances can avoid the situation that some counter? intuitive and irrational results are produced and is more effective, reasonable and advanced than other similar methods. Besides, the proposed method of measuring distances between PFSs is applied in a real environment of application which is the medical diagnosis and is compared with other previous methods to demonstrate its superiority and efficiency. And the feasibility of the proposed method in handling uncertainties in practice is also proved at the same time.