Generalized Logarithmic Similarity Measure for Intuitionistic Fuzzy Sets and Applications for Medical Diagnosis, Pattern Recognition and Decision-Making

Abstract

Intuitionistic fuzzy set theory is a modified version of fuzzy set theory, first introduced by Atanassov. The studies show that several similarity measures between Intuitionistic fuzzy sets are obtained, some of which are inappropriate for use in the current situation. In this study, we will create a novel and adaptable approach under the Intuitionistic fuzzy environment for investigating the decision-making process. Additionally, it suggested new logarithmic similarity and weight similarity measures under Intuitionistic fuzzy sets and clarified their validity. The proposed measure satisfies several exquisite features, allowing for implementation in various domains. The proposed similarity measure has been offered for performance in pattern recognition and medical diagnosis problem with several illustrative examples. Some unexpected scenarios have been discussed and analysed by using existing similarity measures. Using an example, illustrate the comparison analysis's effectiveness and adaptability. The study findings might offer vital information to the required stakeholders in various disciplines. A graph of the developed study is displayed to illustrate the challenges in medical diagnosis between patients and diseases. Finally, we demonstrated the MADM method for enhancing the college selection problem by drawing numerical illustration and making a graphical representation between them.