Using data mining for assessing diagnosis of breast cancer

The capability of the classification SVM, Tree Boost and Tree Forest in analyzing the DDSM dataset was investigated for the extraction of the mammographic mass features along with age that discriminates true and false cases. In the present study, SVM technique shows promising results for increasing diagnostic accuracy of classifying the cases witnessed by the largest area under the ROC curve (area under empirical ROC curve =0.79768 and area under binomial ROC curve = 0.85323) comparable to empirical ROC and binomial ROC of 0.57575 and 0.58548 for tree forest while least empirical ROC and binomial ROC of 0.53452 and 0.53882 was accounted by tree boost. These results are confirmed by SVM average gain of 1.7323, tree forest average gain of 1.5576 and tree boost average gain of 1.5718.