A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical Diagnoses

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 fro...

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Main Authors: Chao Zhang, Deyu Li, Yan Yan
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Computational and Mathematical Methods in Medicine
Online Access:http://dx.doi.org/10.1155/2015/292710
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spelling doaj-ec158f7bcb454f339deeaad683d1b2432020-11-24T21:20:17ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182015-01-01201510.1155/2015/292710292710A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical DiagnosesChao Zhang0Deyu Li1Yan Yan2School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, ChinaSchool of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, ChinaSchool and Hospital of Stomatology, Peking University, Beijing 100089, ChinaIn 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.http://dx.doi.org/10.1155/2015/292710
collection DOAJ
language English
format Article
sources DOAJ
author Chao Zhang
Deyu Li
Yan Yan
spellingShingle Chao Zhang
Deyu Li
Yan Yan
A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical Diagnoses
Computational and Mathematical Methods in Medicine
author_facet Chao Zhang
Deyu Li
Yan Yan
author_sort Chao Zhang
title A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical Diagnoses
title_short A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical Diagnoses
title_full A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical Diagnoses
title_fullStr A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical Diagnoses
title_full_unstemmed A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical Diagnoses
title_sort dual hesitant fuzzy multigranulation rough set over two-universe model for medical diagnoses
publisher Hindawi Limited
series Computational and Mathematical Methods in Medicine
issn 1748-670X
1748-6718
publishDate 2015-01-01
description 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.
url http://dx.doi.org/10.1155/2015/292710
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