On the breast mass diagnosis using Bayesian networks

Abstract

Nowadays, breast cancer is considered a significant health problem in Mexico. Mammogram is an effective study for detecting mass lesions, which could indicate this disease. However, due to the density of breast tissue and a wide range of mass characteristic, the mass diagnosis is difficult. In this study, the performance comparison of Bayesian networks models on classification of benign and malignant masses is presented. Here, Naïve Bayes, Tree Augmented Naïve Bayes, K-dependence Bayesian classifier, and Forest Augmented Naïve Bayes models are analyzed. Two data sets extracted from the public BCDR-F01 database, including 112 benign and 119 malignant masses, were used to train the models. The experimental results have shown that TAN, KDB, and FAN models with a subset of only eight features have achieved a performance of 0.79 in accuracy, 0.80 in sensitivity, and 0.77 in specificity. Therefore, these models which allow dependencies among variables (features), are considered as suitable and promising methods for automated mass classification.