Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/52509

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dc.contributor.authorFernandes, Filipepor
dc.contributor.authorVicente, Henriquepor
dc.contributor.authorAbelha, Antóniopor
dc.contributor.authorMachado, José Manuelpor
dc.contributor.authorNovais, Paulopor
dc.contributor.authorNeves, Josépor
dc.date.accessioned2018-03-15T15:03:33Z-
dc.date.issued2015-09-02-
dc.identifier.citationFernandes, F., Vicente, H., Abelha, A., Machado, J., Novais, P., & Neves, J. (2015, July). Artificial neural networks in diabetes control. In Science and Information Conference (SAI), 2015 (pp. 362-370). IEEEpor
dc.identifier.isbn978-1-4799-8546-3-
dc.identifier.urihttp://hdl.handle.net/1822/52509-
dc.description.abstractDiabetes Mellitus is now a prevalent disease in both developed and underdeveloped countries, being a major cause of morbidity and mortality. Overweight/obesity and hypertension are potentially modifiable risk factors for diabetes mellitus, and persist during the course of the disease. Despite the evidence from large controlled trials establishing the benefit of intensive diabetes management in reducing microvasculars and macrovasculars complications, high proportions of patients remain poorly controlled. Poor and inadequate glycemic control among patients with Type 2 diabetes constitutes a major public health problem and a risk factor for the development of diabetes complications. In clinical practice, optimal glycemic control is difficult to obtain on a long-term basis, once the reasons for feebly glycemic control are complex. Therefore, this work will focus on the development of a diagnosis support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centred on Artificial Neural Networks, to evaluate the Diabetes states and the Degree-of-Confidence that one has on such a happening.por
dc.description.sponsorshipFCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/ UI0752/2014por
dc.language.isoengpor
dc.publisherInstitute of Electrical and Electronics Engineers Inc.por
dc.rightsrestrictedAccesspor
dc.subjectArtificial Neural Networkspor
dc.subjectDegree-of-Confidencepor
dc.subjectDiabetes Mellituspor
dc.subjectLogic Programmingpor
dc.subjectQuality-of-Informationpor
dc.titleArtificial neural networks in diabetes controlpor
dc.typeconferencePaperpor
dc.peerreviewedyespor
dc.relation.publisherversionhttp://ieeexplore.ieee.org/abstract/document/7237169/?reload=truepor
oaire.citationStartPage362por
oaire.citationEndPage370por
oaire.citationConferencePlaceLondon, UKpor
dc.date.updated2018-03-01T14:01:07Z-
dc.identifier.doi10.1109/SAI.2015.7237169por
dc.identifier.esbn978-1-4799-8547-0-
dc.subject.fosCiências Naturais::Ciências da Computação e da Informaçãopor
dc.description.publicationversioninfo:eu-repo/semantics/publishedVersionpor
dc.subject.wosScience & Technology-
sdum.export.identifier3121-
sdum.conferencePublicationProceedings of the 2015 Science and Information Conference, SAI 2015por
sdum.bookTitle2015 SCIENCE AND INFORMATION CONFERENCE (SAI)por
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