Medical Decision Making Using Generalized Interval-Valued Fuzzy Numbers

Real world problems are often ill defined due to uncertainty caused mainly by the deficiency of precision and data, diminutive sample sizes or data acquired from specialist opinions, artificial/man-made errors, etc. To handle uncertainties of these types, type-I fuzzy set theory (FST) has been more often explored. However, it is not always possible for type-I FST to characterize uncertainty in all real world problems. Examples may be had in the diagnosis of patients that has to be mostly dependent on the buoyancy level of a medical investigator (MI) with a very limited degree of accuracy. In such circumstances, interval-valued fuzzy set (IVFS) is a more suitable apparatus to embody vague linguistic expressions of the patients. Moreover, it has the ability to incorporate the complete degree of confidence of the medical investigator’s opinion in certain closed region (i.e. interval). This paper presents a maiden attempt to swot up decision making for MIs using arithmetic of triangular generalized interval-valued fuzzy numbers (TGIVFNs). The TGIVFN has the ability to deal with sequence of things that account for the parameter height of TGIVFN. Parameter heights of TGIVFN characterize the grade of buoyancy of judgments of the decision makers in a very specific comportment. Finally, a case study has been carried out under this setting using live data in order to exhibit the proposed techniques. It is observed that the proposed approach provides a way to perform MI in an explicit demeanor which presents acceptable consequences in comparison to classical techniques. Furthermore, it provides more precision to the decision of the medical investigators.