Similarity measures of intuitionistic fuzzy soft sets and their decision making

TL;DR

This paper introduces new similarity measures for intuitionistic fuzzy soft sets and applies them to a medical diagnosis decision-making process, demonstrating potential improvements with clinical data integration.

Contribution

It proposes novel similarity measures for IFS-sets and develops a decision method for medical diagnosis based on these measures.

Findings

Effective decision-making method demonstrated on medical diagnosis

Potential for improved diagnosis accuracy with clinical data

Illustrative examples show practical applicability

Abstract

In this article, we define some types of distances between two intuitionistic fuzzy soft (IFS) sets and proposed similarity measures of two IFS-sets. We then construct a decision method which is applied to a medical diagnosis problem that is based on similarity measures of IFS-sets. Finally we give two simple example to show the possibility of using this method for diagnosis of diseases which could be improved by incorporating clinical results and other competing diagnosis.