Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/39025

TitleLogic programming and artificial neural networks in breast cancer detection
Author(s)Guimarães, T.
Gomes, S.
Vicente, H.
Santos, M. F.
Neves, José
Machado, José Manuel
Novais, Paulo
KeywordsBreast cancer
Tyrer-cuzick model
Knowledge representation and reasoning
Logic programing
Artificial Neural Networks
Issue date2015
PublisherSpringer
JournalLecture Notes in Computer Science
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).
Abstract(s)About 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.
TypeConference paper
URIhttp://hdl.handle.net/1822/39025
ISBN9783319192215
DOI10.1007/978-3-319-19222-2_18
ISSN0302-9743
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CCTC - Artigos em revistas internacionais
DI/CCTC - Artigos (papers)

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