## 4 Discussion
it was mentioned that the majority of results share the same outcomes. Table 78 presents the former works that used the same data (Tables 1 and 2). In previous studies [4; 5; 10; 11] patients p\({}_{1}\), p\({}_{3}\), and p\({}_{4}\) suffered from malaria fever while patient p\({}_{2}\) suffered from stomach problems. Also in these studies, the same methodology was used to derive the results, with the difference being that in the current study the data combination was used so that there was more than one expert's opinion. In IFS (q = 1), the aggregation operators arithmetic mean, harmonic mean, Bellaman/Zadeh, I-OWA, and q-ROFWA brought the same results as the previous studies. In PFS (q = 2), the aggregation operators arithmetic mean, harmonic mean, Bellaman/Zadeh, and I-OWA had the same results, but the operator q-ROFWA brought different results. Similarly, the FFS (q = 3), the aggregation operators arithmetic mean, harmonic mean, Bellaman/Zadeh and I--OWA showed the same results, but the operator q-ROFWA showed different results. If there is more than one maximum value, it is not clear which disease is more dominant. Therefore, the operator q-ROFWA seemed to add more ambiguity to the data in order to differentiate them. Another point worth noting is that the values of the results decreased as q increased, and the reason for this is that more ambiguity was introduced into the system as q increased [4]. Lastly, there were the similarity measures for making a medical decision. The results were compared with various methods [11; 17; 18; 23]. There are some extra methods beyond similarity measures, such as distance and divergence measures. These three methods are interrelated. Three similarity measures were used. While applying the cosine and the Peng similarity it was observed that they shared the same results: P\({}_{1}\) was suffering from viral fever, P\({}_{2}\) was suffering from stomach problems, P\({}_{3}\) was suffering from typhoid fever, and P\({}_{4}\) was suffering from viral fever. In former works [17; 18; 23] a likeness can be observed. However, in some cases, results were different because there were other similarity/divergence measures such as \(\widetilde{\text{S}}\)-measure, D\({}_{\text{WY}(\text{a})}\) (AB) and [PERSON]'s method that changed the results. In the last case of similarity measures (q-ROFC) it was noticed that there were some different results due to the introduction of the degree of indeterminacy \(\pi\) which included the uncertainty of the fuzzy sets. Regarding q = 1, P\({}_{1}\) was \begin{table}
\begin{tabular}{c|c|c c c} \hline \hline  & & **P\({}_{1}\)** & **P\({}_{2}\)** & **P\({}_{3}\)** & **P\({}_{4}\)** \\ \hline \multicolumn{5}{c}{q-ROFSs and Sanchez’s Method} \\ \hline
[4; 10; 11] & q = 1 & Malaria & stomach & Malaria & Malaria \\  & & fever & problems & fever \\
[4; 5] & q = 2 & Malaria & stomach & Malaria & Malaria \\  & & fever & problems & fever & fever \\
[4] & q = 3 & Malaria & stomach & Malaria & Malaria \\  & & fever & problems & fever & fever \\ \hline \multicolumn{5}{c}{Distances} \\ \hline
[11] & Hamming/ & Malaria & stomach & Typhoid & Viral \\  & Euclidean & fever & problems & fever & fever \\ \hline \multicolumn{5}{c}{Similarity Measures} \\ \hline
[23] & \(\widetilde{\text{S}}\) & Malaria & Stomach & Typhoid & Viral \\  & & fever & problems & fever & fever \\
[23] & \(\widetilde{\text{S}}\) & Viral & Stomach & Typhoid & Viral \\  & & fever & problems & fever & fever \\ \hline \multicolumn{5}{c}{Divergence Measures} \\ \hline
[17] & PIFDM & Viral & Stomach & Typhoid & Viral \\  & & fever & problems & fever & fever \\
[17] & D\({}_{\text{WY}(\text{a})}\) (AB) & Malaria & Stomach & Typhoid & Viral \\  & & fever & problems & fever & fever \\
[18] & PFSDM & Viral & Stomach & Typhoid & Viral \\  & & fever & problems & fever & fever \\
[18] & Xiao’smethod & Malaria & Stomach & Typhoid & Viral \\  & & fever & problems & fever & fever \\ \hline \hline \end{tabular}
\end{table} 
Table 78: Results from former works.