dc.contributor.author |
Cruz-Ramirez, N |
|
dc.contributor.author |
Acosta-Mesa, HG |
|
dc.contributor.author |
Carrillo-Calvet, H |
|
dc.contributor.author |
Nava-Fernandez, LA |
|
dc.contributor.author |
Barrientos-Martínez, RE |
|
dc.date.accessioned |
2011-01-22T10:26:14Z |
|
dc.date.available |
2011-01-22T10:26:14Z |
|
dc.date.issued |
2007 |
|
dc.identifier.issn |
0010-4825 |
|
dc.identifier.uri |
http://hdl.handle.net/11154/1085 |
|
dc.description.abstract |
We evaluate the effectiveness of seven Bayesian network classifiers as potential tools for the diagnosis of breast cancer using two real-world databases containing fine-needle aspiration of the breast lesion cases collected by a single observer and multiple observers, respectively. The results show a certain ingredient of subjectivity implicitly contained in these data: we get an average accuracy of 93.04% for the former and 83.31% for the latter. These findings suggest that observers see different things when looking at the samples in the microscope |
en_US |
dc.description.abstract |
a situation that significantly diminishes the performance of these classifiers in diagnosing such a disease. (C) 2007 Elsevier Ltd. All rights reserved. |
en_US |
dc.language.iso |
en |
en_US |
dc.title |
Diagnosis of breast cancer using Bayesian networks: A case study |
en_US |
dc.type |
Article |
en_US |
dc.identifier.idprometeo |
1071 |
|
dc.identifier.doi |
10.1016/j.compbiomed.2007.02.003 |
|
dc.source.novolpages |
37(11):1553-1564 |
|
dc.subject.wos |
Biology |
|
dc.subject.wos |
Computer Science, Interdisciplinary Applications |
|
dc.subject.wos |
Engineering, Biomedical |
|
dc.subject.wos |
Mathematical & Computational Biology |
|
dc.description.index |
WoS: SCI, SSCI o AHCI |
|
dc.subject.keywords |
fine-needle aspiration of the breast |
|
dc.subject.keywords |
cytodiagnosis of breast cancer |
|
dc.subject.keywords |
Bayesian network classifiers |
|
dc.subject.keywords |
interobserver variability |
|
dc.relation.journal |
Computers in Biology and Medicine |
|