Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/90532 |
Resumo: | Crohn's disease (CD) diagnosis is a tremendously serious health problem due to its ultimately effect on the gastrointestinal tract that leads to the need of complex medical assistance. In this study, the backpropagation neural network fuzzy classifier and a neuro-fuzzy model are combined for diagnosing the CD. Factor analysis is used for data dimension reduction. The effect on the system performance has been investigated when using fuzzy partitioning and dimension reduction. Additionally, further comparison is done between the different levels of the fuzzy partition to reach the optimal performance accuracy level. The performance evaluation of the proposed system is estimated using the classification accuracy and other metrics. The experimental results revealed that the classification with level-8 partitioning provides a classification accuracy of 97.67 %, with a sensitivity and specificity of 96.07 and 100 %, respectively. |
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Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approachCiências Tecnológicas, Ciências médicas e da saúdeTechnological sciences, Medical and Health sciencesCrohn's disease (CD) diagnosis is a tremendously serious health problem due to its ultimately effect on the gastrointestinal tract that leads to the need of complex medical assistance. In this study, the backpropagation neural network fuzzy classifier and a neuro-fuzzy model are combined for diagnosing the CD. Factor analysis is used for data dimension reduction. The effect on the system performance has been investigated when using fuzzy partitioning and dimension reduction. Additionally, further comparison is done between the different levels of the fuzzy partition to reach the optimal performance accuracy level. The performance evaluation of the proposed system is estimated using the classification accuracy and other metrics. The experimental results revealed that the classification with level-8 partitioning provides a classification accuracy of 97.67 %, with a sensitivity and specificity of 96.07 and 100 %, respectively.2017-012017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfimage/pnghttps://hdl.handle.net/10216/90532eng0140-011810.1007/s11517-016-1508-7Sk. Saddam AhmedNilanjan DeyAmira S. AshourDimitra Sifaki-PistollaDana Balas-TimarValentina E. BalasJoão Manuel R. S. Tavaresinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T14:41:03Zoai:repositorio-aberto.up.pt:10216/90532Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:06:44.526054Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach |
title |
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach |
spellingShingle |
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach Sk. Saddam Ahmed Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
title_short |
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach |
title_full |
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach |
title_fullStr |
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach |
title_full_unstemmed |
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach |
title_sort |
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach |
author |
Sk. Saddam Ahmed |
author_facet |
Sk. Saddam Ahmed Nilanjan Dey Amira S. Ashour Dimitra Sifaki-Pistolla Dana Balas-Timar Valentina E. Balas João Manuel R. S. Tavares |
author_role |
author |
author2 |
Nilanjan Dey Amira S. Ashour Dimitra Sifaki-Pistolla Dana Balas-Timar Valentina E. Balas João Manuel R. S. Tavares |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Sk. Saddam Ahmed Nilanjan Dey Amira S. Ashour Dimitra Sifaki-Pistolla Dana Balas-Timar Valentina E. Balas João Manuel R. S. Tavares |
dc.subject.por.fl_str_mv |
Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
topic |
Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
description |
Crohn's disease (CD) diagnosis is a tremendously serious health problem due to its ultimately effect on the gastrointestinal tract that leads to the need of complex medical assistance. In this study, the backpropagation neural network fuzzy classifier and a neuro-fuzzy model are combined for diagnosing the CD. Factor analysis is used for data dimension reduction. The effect on the system performance has been investigated when using fuzzy partitioning and dimension reduction. Additionally, further comparison is done between the different levels of the fuzzy partition to reach the optimal performance accuracy level. The performance evaluation of the proposed system is estimated using the classification accuracy and other metrics. The experimental results revealed that the classification with level-8 partitioning provides a classification accuracy of 97.67 %, with a sensitivity and specificity of 96.07 and 100 %, respectively. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01 2017-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/90532 |
url |
https://hdl.handle.net/10216/90532 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0140-0118 10.1007/s11517-016-1508-7 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf image/png |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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