Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/39025

TítuloLogic programming and artificial neural networks in breast cancer detection
Autor(es)Neves, José
Guimarães, Tiago
Gomes, Sabino
Vicente, Henrique
Santos, Mariana
Neves, João
Machado, José
Novais, Paulo
Palavras-chaveBreast cancer
Tyrer-cuzick model
Knowledge representation and reasoning
Logic programing
Artificial Neural Networks
Data2015
EditoraSpringer
RevistaLecture Notes in Computer Science
CitaçãoNeves, 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).
Resumo(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.
TipoArtigo em ata de conferência
URIhttps://hdl.handle.net/1822/39025
ISBN9783319192215
DOI10.1007/978-3-319-19222-2_18
ISSN0302-9743
Arbitragem científicayes
AcessoAcesso aberto
Aparece nas coleções:CCTC - Artigos em revistas internacionais
DI/CCTC - Artigos (papers)

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