A robust framework for medical diagnostics based on interval valued Q-neutrosophic soft sets with aggregation operators

Abstract

The best way to deal with complicated life scenarios that accompany the decision-making 
process is to update previous concepts constantly. Therefore, researchers must constantly discover 
powerful mathematical tools that suit the accompanying circumstances. In this regard, we combine both 
soft set, neutrosophic set, and interval setting under Q-two-dimensional universal information to introduce 
a new hybrid innovative model called interval valued-Q-neutrosophic soft sets. The core goal of this model 
is to keep the features of previous models like soft sets, neutrosophic sets, and Q-Fuzzy sets in dealing with 
the lack of uncertainty and neutrality associated with real-life issues. This new approach allows decision
makers to employ interval-valued form with Q-two-dimensional universal information, which provides 
them with more stability and feasibility in describing uncertain information more completely and 
accurately. Under the our propose model, we discuss effectively set-theory operations such as subset, 
union, intersection, complement, AND operation, and OR operation for interval valued-Q-neutrosophic 
soft sets, as well as some special operations like the necessity and possibility operations of an interval 
valued-Q-neutrosophic soft sets. In addition, we presented many properties supported by numerical 
examples that explain how they work. Finally, this new model has been successfully tested in dealing with 
one of the medical diagnostic problems based on hypothetical data for a respiratory disease. Building an 
algorithm based on the aggregation operator for interval valued-Q-neutrosophic soft set data solved this 
issue (i.e., selecting the optimal alternative). 

DOI: 10.5281/zenodo.11479120