2015
Neves, J.; Guimarães, T.; Gomes, S.; Vicente, H.; Santos, M.; Neves, J.; Machado, J.; Novais, P.
Logic programming and artificial neural networks in breast cancer detection Proceedings Article
Em: I., Catala A. Joya G. Rojas (Ed.): pp. 211-224, Springer Verlag, 2015, ISSN: 03029743, (cited By 9; Conference of 13th International Work-Conference on Artificial Neural Networks, IWANN 2015 ; Conference Date: 10 June 2015 Through 12 June 2015; Conference Code:119669).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Computation theory; Decision support systems; Diseases; Knowledge representation; Neural networks; Risk assessment, Breast Cancer; Breast cancer detection; Breast cancer risk assessments; Degree of confidence; Formal framework; Hybrid decision support systems; Knowledge representation and reasoning; Logic programing, Logic programming
@inproceedings{Neves2015211,
title = {Logic programming and artificial neural networks in breast cancer detection},
author = {J. Neves and T. Guimarães and S. Gomes and H. Vicente and M. Santos and J. Neves and J. Machado and P. Novais},
editor = {Catala A. Joya G. Rojas I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937690305&doi=10.1007%2f978-3-319-19222-2_18&partnerID=40&md5=4892cc38432d002443cd457e3c466108},
doi = {10.1007/978-3-319-19222-2_18},
issn = {03029743},
year = {2015},
date = {2015-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9095},
pages = {211-224},
publisher = {Springer Verlag},
abstract = {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. © Springer International Publishing Switzerland 2015.},
note = {cited By 9; Conference of 13th International Work-Conference on Artificial Neural Networks, IWANN 2015 ; Conference Date: 10 June 2015 Through 12 June 2015; Conference Code:119669},
keywords = {Artificial intelligence; Computation theory; Decision support systems; Diseases; Knowledge representation; Neural networks; Risk assessment, Breast Cancer; Breast cancer detection; Breast cancer risk assessments; Degree of confidence; Formal framework; Hybrid decision support systems; Knowledge representation and reasoning; Logic programing, Logic programming},
pubstate = {published},
tppubtype = {inproceedings}
}