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dc.contributor.authorNeves, Josépor
dc.contributor.authorGuimarães, Tiagopor
dc.contributor.authorGomes, Sabinopor
dc.contributor.authorVicente, Henriquepor
dc.contributor.authorSantos, Marianapor
dc.contributor.authorNeves, Joãopor
dc.contributor.authorMachado, Josépor
dc.contributor.authorNovais, Paulo-
dc.date.accessioned2015-12-15T16:49:12Z-
dc.date.available2015-12-15T16:49:12Z-
dc.date.issued2015-
dc.identifier.citationNeves, J., Guimarães, T., Gomes, S., Vicente, H., Santos, M., Machado, J., & Novais, P. (2015) Logic programming and artificial neural networks in breast cancer detection. Vol. 9095. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 211-224).por
dc.identifier.isbn9783319192215por
dc.identifier.issn0302-9743por
dc.identifier.urihttps://hdl.handle.net/1822/39025-
dc.description.abstractAbout 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.por
dc.description.sponsorshipThis work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013.por
dc.language.isoengpor
dc.publisherSpringerpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147280/PTpor
dc.rightsopenAccesspor
dc.subjectBreast cancerpor
dc.subjectTyrer-cuzick modelpor
dc.subjectKnowledge representation and reasoningpor
dc.subjectLogic programingpor
dc.subjectArtificial Neural Networkspor
dc.titleLogic programming and artificial neural networks in breast cancer detectionpor
dc.typeconferencePaperpor
dc.peerreviewedyespor
sdum.publicationstatuspublishedpor
oaire.citationStartPage211por
oaire.citationEndPage224por
oaire.citationTitleLecture Notes in Computer Science (LNCS)por
oaire.citationVolume9095por
dc.date.updated2015-12-10T17:48:03Z-
dc.identifier.doi10.1007/978-3-319-19222-2_18por
dc.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informáticapor
dc.subject.wosScience & Technologypor
sdum.journalLecture Notes in Computer Sciencepor
sdum.conferencePublicationIWANN 2015, Part II, LNCS 9095, pp. 211–224, 2015.-
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