## 1 Introduction
### Related Works  
Fuzzy sets have been applied in the past to solve many medical applications. They have also been applied in various ways. Using neuroimaging and hierarchical fuzzy models, a computational model for the diagnosis of burnout was constructed. Burnout is complex enough to study, but by using artificial intelligence tools it can be approximated and diagnosed [6]. A more extended application concerning medical diagnosis is to find the disease corresponding to a patient given fuzzy relations. There are various ways of using fuzzy sets in making a medical decision. [PERSON] extended the theory of fuzzy sets by introducing the Z-numbers. A Z-number is an ordered pair of fuzzy numbers that restricts the reliability and evaluation of human judgment. The first fuzzy number is the uncertainty of information in evaluation and the second fuzzy number represents the reliability. Z-numbers have been applied to medical diagnosis problems. However, a more distinct study for Z-number weights is needed, as well as the idea of a negative human-decision approach is necessary to be incorporated [7]. In [8], a formula named compositional rule of inference (CRI) has been applied to fuzzy sets for making a medical decision. In this case, the term \"multi-criteria\" is introduced by extracting a result through many components. Another formula using multi-criteria is the Preference ranking organization method for the enrichment of evaluation (PROMETHEE) [9]. This method can cope with complex and multiple criteria, and it depends on the information and the weight of the criteria. The PROMETHEE model can be extended to cope with incomplete weight information. In [4; 5; 10; 11], decision-making must be taken by q-ROFS and applying [PERSON]'s approach. This method uses fuzzy relations and compositions in order to achieve the medical diagnosis; it is a method that solves multi-criteria problems as well [9]. In [12; 13], more complicated forms of fuzzy sets are mentioned, adapting human language more effectively. Bipolar fuzzy sets [12] are complex sets that cope with positive and negative human opinions, while picture fuzzy sets [13] consist of three parameters: positive degree, neutral degree, and negative degree. The concept of spherical fuzzy sets (SFSs) [14] is an extension of PFS which includes three degrees, membership, abstinence, and non-membership, and it is more flexible than q-ROFSs. Distance is another method in order to make a medical decision. Pattern recognition uses distances and similarity measures to estimate which pattern is closer to a specific class (like a disease). [PERSON] and [PERSON] [11] used Euclidean and Hamming distances which are the most common distances for classification and pattern recognition. In [15], a new distance measure is introduced. Using matrix norms, this distance measure has better performance than the common distances. Other distance measures are presented in [16]. FFSH distance is based on the Hellinger distance and FFSTD distance is based on the triangular divergence. These distances are applied to q-ROFS for q = 3 (FFS) and have a better performance as well. In [17; 18; 19], an alternative measure is proposed. Divergence measures for advanced fuzzy sets (q-ROFS) have played an important role in decreasing uncertainty and making decision-making with optimum accuracy [17]. Divergence measures are distances with divergence perspective and, thus, the larger the divergence measure distance, the more different objects (fuzzy sets), and the smaller the divergence measure distance, the more similar objects (fuzzy sets) [18]. In [17], a parametric intuitionistic fuzzy divergence measure (PIFDM distance) was analyzed. (PFSDM distance) was analyzed, in [18] a divergence measure of PFSs (PFSDM distance) was analyzed, and in [19] the Jensen-Shannon divergence of PFSs (PFS)S distance) was analyzed. In general, divergence measures have to be used for more applications such as pattern recognition and multi-criteria decision-making problems.