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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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Detalles Bibliográficos
Autores principales: Zhang, Chao, Li, Deyu, Yan, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4691518/
https://www.ncbi.nlm.nih.gov/pubmed/26858772
http://dx.doi.org/10.1155/2015/292710
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author Zhang, Chao
Li, Deyu
Yan, Yan
author_facet Zhang, Chao
Li, Deyu
Yan, Yan
author_sort Zhang, Chao
collection PubMed
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.
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spelling pubmed-46915182016-02-08 A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical Diagnoses Zhang, Chao Li, Deyu Yan, Yan Comput Math Methods Med Research Article 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. Hindawi Publishing Corporation 2015 2015-12-09 /pmc/articles/PMC4691518/ /pubmed/26858772 http://dx.doi.org/10.1155/2015/292710 Text en Copyright © 2015 Chao Zhang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Chao
Li, Deyu
Yan, Yan
A Dual Hesitant Fuzzy Multigranulation Rough Set over Two-Universe Model for Medical Diagnoses
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4691518/
https://www.ncbi.nlm.nih.gov/pubmed/26858772
http://dx.doi.org/10.1155/2015/292710
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