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Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica

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dc.contributor.authorCarvalho, V. O. [UNESP]
dc.contributor.authorNeves, L. A. [UNESP]
dc.contributor.authorDe Godoy, M. F.
dc.contributor.authorMoreira, R. D.
dc.contributor.authorMoriel, A. R.
dc.contributor.authorMurta, L. O.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionFaculdade de Medicina de São José do Rio Preto (FAMERP)
dc.contributor.institutionInstituto de Anatomia Patológica e Citopatologia
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.date.accessioned2014-05-27T11:28:42Z
dc.date.available2014-05-27T11:28:42Z
dc.date.issued2013-03-26
dc.description.abstractThis work combines symbolic machine learning and multiscale fractal techniques to generate models that characterize cellular rejection in myocardial biopsies and that can base a diagnosis support system. The models express the knowledge by the features threshold, fractal dimension, lacunarity, number of clusters, spatial percolation and percolation probability, all obtained with myocardial biopsies processing. Models were evaluated and the most significant was the one generated by the C4.5 algorithm for the features spatial percolation and number of clusters. The result is relevant and contributes to the specialized literature since it determines a standard diagnosis protocol. © 2013 Springer.en
dc.description.affiliationUniversidade Estadual Paulista DEMAC, Rio Claro
dc.description.affiliationNUTECC Famerp, São José do Rio Preto
dc.description.affiliationFaculdade de Medicina de São José Do Rio Preto, São José do Rio Preto
dc.description.affiliationInstituto de Anatomia Patológica e Citopatologia, São José do Rio Preto
dc.description.affiliationUniversidade de São Paulo FFCLRP Depto. Computação e Matemática, Ribeirão Preto
dc.description.affiliationUnespUniversidade Estadual Paulista DEMAC, Rio Claro
dc.format.extent272-275
dc.identifierhttp://dx.doi.org/10.1007/978-3-642-21198-0_70
dc.identifier.citation5th Latin American Congress on Biomedical Engineering (claib 2011): Sustainable Technologies For the Health of All, Pts 1 and 2. New York: Springer, v. 33, n. 1-2, p. 272-275, 2013.
dc.identifier.doi10.1007/978-3-642-21198-0_70
dc.identifier.issn1680-0737
dc.identifier.lattes1961581092362881
dc.identifier.scopus2-s2.0-84875250024
dc.identifier.urihttp://hdl.handle.net/11449/74875
dc.language.isopor
dc.relation.ispartofIFMBE Proceedings
dc.relation.ispartofsjr0,143
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectmultiscale fractal techniques
dc.subjectmyocardial biopsies images
dc.subjectsymbolic machine learning
dc.subjectC4.5 algorithm
dc.subjectDiagnosis support systems
dc.subjectLacunarity
dc.subjectMultiscale fractals
dc.subjectNumber of clusters
dc.subjectPercolation probability
dc.subjectSymbolic machine learning
dc.subjectBiomedical engineering
dc.subjectFractal dimension
dc.subjectLearning systems
dc.subjectSolvents
dc.subjectBiopsy
dc.titleAprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdicapt
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.springer.com/open+access/authors+rights
dspace.entity.typePublication
unesp.author.lattes1961581092362881
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Geociências e Ciências Exatas, Rio Claropt
unesp.departmentEstatística, Matemática Aplicada e Computação - IGCEpt

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Rio Claro - IGCE - Instituto de Geociências e Ciências Exatas