Diagnosis of breast cancer using Bayesian networks: a case study.
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.
Cruz-Ramírez
Nicandro
N
Facultad de Física e Inteligencia Artificial, Universidad Veracruzana, Sebastián Camacho 5, Col. Centro, C. P. 91000 Xalapa, Veracruz, Mexico. ncruz@uv.mx
Acosta-Mesa
Héctor Gabriel
HG
Carrillo-Calvet
Humberto
H
Nava-Fernández
Luis Alonso
LA
Barrientos-Martínez
Rocío Erandi
RE
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