Fuzzy Chest Pain Assessment for Unstable Angina based on Braunwald Symptomatic and Obesity Clinical Conditions

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dc.contributor.author Orsi, Thiago
dc.contributor.author Araujo, Ernesto [UNIFESP]
dc.contributor.author Simoes, Ricardo
dc.contributor.author IEEE
dc.date.accessioned 2018-06-15T11:14:49Z
dc.date.available 2018-06-15T11:14:49Z
dc.date.issued 2014-01-01
dc.identifier http://dx.doi.org/10.1109/FUZZ-IEEE.2014.6891836
dc.identifier.citation 2014 Ieee International Conference On Fuzzy Systems (fuzz-ieee). New York: Ieee, p. 1076-1082, 2014.
dc.identifier.issn 1544-5615
dc.identifier.uri http://repositorio.unifesp.br/11600/41937
dc.description.abstract A fuzzy medical diagnostic decision system for helping support to evaluate patients with anginal chest pain and obesity clinical condition is proposed in this paper. Such an approach is based on the Braunwald symptomatic classification, the fuzzy set theory and fuzzy logic, and a risk obesity factor determined by a simplified Fuzzy Body Mass Index (FBMI). The fuzzy Braunwald symptomatic classification intertwined with the fuzzy obesity risk factor overwhelm the current rapid access chest pain clinic approaches that do not discriminate the obesity comorbidity or takes into account the subjectiveness, uncertainty, imprecision, and vagueness concerning such a clinical health condition. The resulting fuzzy obesity-based Braunwald symptomatic chest pain assessment is an alternative to support healthcare professionals in primary health care for patients with anginal chest pain worsened by the obesity clinical condition. en
dc.format.extent 1076-1082
dc.language.iso eng
dc.publisher Ieee
dc.relation.ispartof 2014 Ieee International Conference On Fuzzy Systems (fuzz-ieee)
dc.rights Acesso restrito
dc.title Fuzzy Chest Pain Assessment for Unstable Angina based on Braunwald Symptomatic and Obesity Clinical Conditions en
dc.type Trabalho apresentado em evento
dc.contributor.institution FCMMG
dc.contributor.institution Universidade Federal de São Paulo (UNIFESP)
dc.contributor.institution Inteligencia Artificial Med Ltda IAMED
dc.description.affiliation FCMMG, Med Grad Dept, Belo Horizonte, MG, Brazil
dc.description.affiliation FCMMG, Postgrad & Res Inst IPG, Belo Horizonte, MG, Brazil
dc.description.affiliation Univ Fed Sao Paulo, Hlth Informat Dept DIS, Sao Paulo, SP, Brazil
dc.description.affiliation Inteligencia Artificial Med Ltda IAMED, Sao Jose Dos Campos, SP, Brazil
dc.description.affiliationUnifesp Univ Fed Sao Paulo, Hlth Informat Dept DIS, Sao Paulo, SP, Brazil
dc.identifier.doi 10.1109/FUZZ-IEEE.2014.6891836
dc.description.source Web of Science
dc.identifier.wos WOS:000350793500154



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