Evaluation of bipolar fuzzy soft sets in decision-making with a new approach

The importance of the negative and positive degrees of the corresponding feature of bipolar fuzzy sets has been mentioned. The properties of bipolar fuzzy sets in fuzzy uncertainty environments are studied. This article proposes that evaluating the maximum of the parameter and entropy values together is the best choice in the decision process. By defining an entropy measure in bipolar fuzzy soft sets, an algorithm has been created by bringing a new approach to the decision process in cases of uncertainty. The steps of the algorithm are followed and analyzed, and results are found, meaning is given to the uncertain. The maximums of entropy measurements and parameter values ​​are processed and listed. Then, a real-life example is selected for the new proposed method. The new approach is a study that analyzes symptoms and parameters in mortality and survival values in the hospital environment. The proposed algorithm, using the positive and negative degree evaluation feature of bipolar fuzzy soft sets, is effective in parameter selection. This can be seen in real-life examples. Ultimately, the best few decision-making options are determined by the algorithm created using the positive and negative values ​​of bipolar fuzzy soft sets. With the designed algorithm, ambiguous data groups are given meaning, and, in summary, it is advised as a method to be applied to data groups with positive and negative degrees.