Analysis of anti-cancer treatment therapies based on TOPSIS method under fractional Diophantine fuzzy Muirhead mean operators.

Cancer affects millions of lives worldwide each year, and finding the most effective treatment is a difficult and important decision for healthcare professionals. This research aims to develop a new method for selecting the most appropriate and affordable cancer treatments. For this purpose, fractional Diophantine fuzzy sets (FDFSs) and the Muirhead mean operator are introduced to deal with uncertainty in medical decision-making. A difficult problem in decision-making is to detect the hidden weight information of criteria and decision-making experts. To solve this problem, the analytical hierarchy process (AHP) is used to calculate unknown weights for both criteria and decision-making experts. Furthermore, a fractional Diophantine fuzzy-TOPSIS (FDF-TOPSIS) technique is proposed to evaluate and select the best treatment process. Next, the proposed FDF-TOPSIS technique is applied to a real-life numerical example of finding the optimal cancer treatment process. Finally, we compare our proposed method with other existing methods and demonstrate the reliability, accuracy, and feasibility of the proposed technique, highlighting its potential to improve decision-making during cancer treatment.