title: A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical Diagnoses.
abstract: In medical science, disease diagnosis is one of the difficult tasks for medical experts who are confronted with challenges in dealing with a lot of uncertain medical information. And different medical experts might express their own thought about the medical knowledge base which slightly differs from other medical experts. Thus, to solve the problems of uncertain data analysis and group decision making in disease diagnoses, we propose a new rough set model called dual hesitant fuzzy multigranulation rough set over two universes by combining the dual hesitant fuzzy set and multigranulation rough set theories. In the framework of our study, both the definition and some basic properties of the proposed model are presented. Finally, we give a general approach which is applied to a decision making problem in disease diagnoses, and the effectiveness of the approach is demonstrated by a numerical example.
authors: School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China; Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Taiyuan, Shanxi 030006, China.
authors: Chao
authors: C
authors: Zhang
authors: School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China; Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Taiyuan, Shanxi 030006, China.
authors: Deyu
authors: D
authors: Li
authors: School and Hospital of Stomatology, Peking University, Beijing 100089, China.
authors: Yan
authors: Y
authors: Yan
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003657:Decision Making; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D017143:Fuzzy Logic; D006801:Humans; D055641:Mathematical Concepts; D008954:Models, Biological; D035501:Uncertainty