## 5 Conclusions  
The decision-making in medicine and the patient's classification of various diseases depends on the expert's interpretation according to symptoms and the medical specialist's experience. Because of the difficulty of considering all of the accurate measurements and symptoms, experts often struggle to make a diagnosis due to imprecise knowledge which causes vagueness and uncertainty in the diagnosis procedure. For handling imprecise knowledge, fuzzy logic is often applied. Fuzzy logic is an important tool of artificial intelligence that methodizes human language while dominating environments characterized by ambiguity, lack of knowledge, and uncertainty. The novelty and contribution of this work relied on the combination of various techniques for intelligent medical diagnosis reasoning. The aim of this paper was to assess the contribution of aggregation operators in q-ROFSs and to apply some extra similarity measures like Peng similarity. In this study, the patient's medical status was represented as q-rung orthopair fuzzy values. The applied methods were as follows: composite fuzzy relation with min-max-min and max-average-min-average using different relation value types, aggregation operators for medical examination, and patient's history based on q-ROFWA, I-OWA, Bellman-Zadeh, Harmonic Mean and Arithmetic Mean, similarity measures based on a cosine measure, and the Peng measure and q-ROFC measure. The results varied and it was uncertain which disease every patient was suffering from. This is due to subjective medical knowledge and, therefore, it was proposed to modify this knowledge so that the result of the diagnosis is acceptable as shown in the flowchart (Figure 2).  
Of course, not all possible ways of making a medical diagnosis with fuzzy logic were studied above. There are several measures of similarity, and several aggregation operators and the q-rung can be extended more. Additionally, the composite fuzzy relation (max-min, min-max) cannot be extended to any set that has more than three components such as picture fuzzy sets. Future work will include fuzzy deep learning and providing more degrees of freedom for optimum handling of medical uncertainty. Moreover, a bigger and more realistic dataset will enrich the medical knowledge and, thus, the relation between symptoms and diseases will be more accurate. In addition, other effective factors in diagnosis are suggested such as sex, age, physical conditions, etc., for accurate decisions. In a disease diagnosis process, another future approach is the fuzzy cognitive map model designed to distinguish a disease properly from other diseases with the same symptoms. The proposed method can be improved further using Interval Type 2 Fuzzy Sets or Z-number to better represent uncertainty enhancing diagnosis accuracy. Regarding aggregation, different types of operators need to be explored in a fuzzy environment, and more experiments are necessary to find the most accurate method for medical diagnosis.  
Figure 2: The flowchart of the Intelligent medical diagnosis reasoning procedure.  
**Author Contributions:** Conceptualization, A.D. and A.S.; methodology, A.D. and A.S.; software, A.S.; validation, A.D. and A.S.; formal analysis, A.D. and A.S.; investigation, A.D. and A.S.; resources, A.D.; data curation, A.D. and A.S.; writing--original draft preparation, A.D. and A.S.; writing--review and editing, A.D. and A.S.; visualization, A.D. and A.S.; supervision, A.D.; project administration, A.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
The authors declare no conflict of interest.