  Mammography is the most effective and available tool for breast cancer
screening. However, the low positive predictive value of breast biopsy
resulting from mammogram interpretation leads to approximately 70% unnecessary
biopsies with benign outcomes. Data mining algorithms could be used to help
physicians in their decisions to perform a breast biopsy on a suspicious lesion
seen in a mammogram image or to perform a short term follow-up examination
instead. In this research paper data mining classification algorithms; Decision
Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM)
are analyzed on mammographic masses data set. The purpose of this study is to
increase the ability of physicians to determine the severity (benign or
malignant) of a mammographic mass lesion from BI-RADS attributes and the
patient,s age. The whole data set is divided for training the models and test
them by the ratio of 70:30% respectively and the performances of classification
algorithms are compared through three statistical measures; sensitivity,
specificity, and classification accuracy. Accuracy of DT, ANN and SVM are
78.12%, 80.56% and 81.25% of test samples respectively. Our analysis shows that
out of these three classification models SVM predicts severity of breast cancer
with least error rate and highest accuracy.
