Abstract: “Mammography is considered as the most effective method for breast cancer screening. It is effective, but it suffers from the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. Recently, several computer-aided diagnosis (CAD) systems have been proposed to reduce the high number of unnecessary breast biopsies. Thus, in this paper, we propose a decision support system for helping the physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. To accomplish this aim, we used a weighted Bayesian classifier. Naïve Bayesian (NB) is known to be the simple classifier and there have been so many applications in the literature. We conduct several experiments to evaluate the performance of the weighted NB on mammographic mass classification database. The experiments were realized with 5-fold cross”

Keywords: NB classifier, Weighted NB classifier, Mammographic Mass Classification, Performance evaluation tests.