title: Diagnosis of breast cancer using Bayesian networks: a case study.
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.
authors: Facultad de Física e Inteligencia Artificial, Universidad Veracruzana, Sebastián Camacho 5, Col. Centro, C. P. 91000 Xalapa, Veracruz, Mexico. ncruz@uv.mx
authors: Nicandro
authors: N
authors: Cruz-Ramírez
authors: Héctor Gabriel
authors: HG
authors: Acosta-Mesa
authors: Humberto
authors: H
authors: Carrillo-Calvet
authors: Luis Alonso
authors: LA
authors: Nava-Fernández
authors: Rocío Erandi
authors: RE
authors: Barrientos-Martínez
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D044963:Biopsy, Fine-Needle; D001943:Breast Neoplasms / Q000175:diagnosis*; D003581:Cytodiagnosis / Q000706:statistics & numerical data; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D015588:Observer Variation