Optimizing Antibiotic Selection for UTIs Using VIKOR and TOPSIS in an Atanassov Intuitionistic Fuzzy MCDM Framework

The measurement of information, along with the assessment of knowledge, plays a crucial role in the theory of Atanassov intuitionistic fuzzy sets (AInFSs). The primary objective of this manuscript is to explore the information and knowledge evaluation of AInF-sets and their application in decision-making (DMI) scenarios. This study introduces a novel knowledge quantification method for AInF-sets, addressing the shortcomings of existing information and knowledge assessment techniques. The reliability and efficiency of the proposed knowledge measurement approach are validated through numerical illustrations, comparing it with current methodologies within the AInF framework. Furthermore, leveraging the suggested knowledge evaluation, an accuracy assessment for AInF-sets is derived. The utilization of the proposed accuracy metric is demonstrated in pattern recognition challenges. To confirm its practical effectiveness, numerical case studies are provided. A refined version of the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) technique, incorporating the proposed accuracy metric, is introduced to tackle a multi-criteria decision-making (MCDM) problem within an intuitionistic fuzzy setting. Finally, a real-world application is presented through a case study focused on selecting the most suitable antibiotic for treating urinary tract infections (UTIs). The efficiency of the suggested method is highlighted by comparing it with prevailing DMI strategies.