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Mammography is the most effective and available tool for breast cancerscreening. However, the low positive predictive value of breast biopsyresulting from mammogram interpretation leads to approximately 70% unnecessarybiopsies with benign outcomes. Data mining algorithms could be used to helpphysicians in their decisions to perform a breast biopsy on a suspicious lesionseen in a mammogram image or to perform a short term follow-up examinationinstead. In this research paper data mining classification algorithms; DecisionTree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM)are analyzed on mammographic masses data set. The purpose of this study is toincrease the ability of physicians to determine the severity (benign ormalignant) of a mammographic mass lesion from BI-RADS attributes and thepatient,s age. The whole data set is divided for training the models and testthem by the ratio of 70:30% respectively and the performances of classificationalgorithms are compared through three statistical measures; sensitivity,specificity, and classification accuracy. Accuracy of DT, ANN and SVM are78.12%, 80.56% and 81.25% of test samples respectively. Our analysis shows thatout of these three classification models SVM predicts severity of breast cancerwith least error rate and highest accuracy.

Predicting the Severity of Breast Masses with Data Mining Methods