@article{
 title = {Diagnosis of breast cancer using Bayesian networks: a case study.},
 type = {article},
 year = {2007},
 keywords = {Algorithms,Bayes Theorem,Biopsy, Fine-Needle,Breast Neoplasms,Breast Neoplasms: diagnosis,Cytodiagnosis,Cytodiagnosis: statistics & numerical data,Databases, Factual,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: statistics & numeric,Female,Humans,Observer Variation},
 pages = {1553-64},
 volume = {37},
 websites = {http://www.sciencedirect.com/science/article/pii/S0010482507000340},
 month = {11},
 id = {47fd16d5-d615-3e6a-8a27-ee52fb556b29},
 created = {2015-04-11T19:52:00.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {838ecfe2-7c01-38b2-970d-875a87910530},
 last_modified = {2017-03-14T14:27:28.880Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
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 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.},
 bibtype = {article},
 author = {Cruz-Ramírez, Nicandro and Acosta-Mesa, Héctor Gabriel and Carrillo-Calvet, Humberto and Nava-Fernández, Luis Alonso and Barrientos-Martínez, Rocío Erandi},
 doi = {10.1016/j.compbiomed.2007.02.003},
 journal = {Computers in biology and medicine},
 number = {11}
}

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