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http://hdl.handle.net/11154/1085
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Title: | Diagnosis of breast cancer using Bayesian networks: A case study |
Authors: | Cruz-Ramirez, N Acosta-Mesa, HG Carrillo-Calvet, H Nava-Fernandez, LA Barrientos-Martínez, RE |
Issue Date: | 2007 |
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 a situation that significantly diminishes the performance of these classifiers in diagnosing such a disease. (C) 2007 Elsevier Ltd. All rights reserved. |
URI: | http://hdl.handle.net/11154/1085 |
ISSN: | 0010-4825 |
Appears in Collections: | Ciencias
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