Some novel distance measures between dual hesitant fuzzy sets and their application in medical diagnosis

A dual hesitant fuzzy set (DHFS) describes the uncertainty in the real world by using the membership degree and nonmembership degree. It can collect fuzzy information comprehensively and apply them into decision-making tasks efficiently. In this article, we extract some characteristics, such as the average function, variance function, hesitancy degree to describe a dual hesitant fuzzy element, and develop novel distance measures of DHFSs based on these characteristics. Further, we investigate their properties and prove the triangle inequality of distance measure. Finally, we apply it in practical medical diagnosis to illustrate the validity of our proposed distance measures.