Using Combined Classifier Based on the Separation of Conventional and Unconventional Samples to Diagnose Breast Cancer

Breast cancer is one of the most common types of cancers in women and in recent years there has been a significant increase in the number of people with this disease. With the increasing spread of science, data mining has become one of the most widely used areas for improving therapeutic systems. In this paper, the diagnosis of breast cancer is performed in two steps. In the first step, an improved genetic algorithm is used to identify the desirable features in the prediction of this disease, and in the second stage, conventional and Unconventional samples are identified to increase the accuracy and create the final classification model. For classification work, a comparison between two decision tree and Support vector machine model is used to show the results of the superiority of the Support vector machine model. The results of the experiments reported the accuracy of breast cancer diagnosis on WBCD, WDBC and WPBC data sets are 99.26%, 98.55% and 98.45%, respectively.