{ Decision tree (DT) is one of the popular and effective data mining methods. DT provides a pathway to find “rules” that could be evaluated for separating the input samples into one of several groups without having to express the functional relationship directly. They avoid the limitations of the parametric models and are well suited for the analysis of nonlinear events. The purpose of this study is to examine the performance of the recent invented DT model algorithm (C5.0) on the diagnosis of breast cancer using cytologically proven tumor dataset . The objective is to classify a tumor as either benign or malignant based on cell descriptions gathered by microscopic examination. The classification performance of C5.0 DT is evaluated and compared to the one that achieved by radial basis function kernel support vector machine (RBF-SVM). The dataset has been partitioned by the ratio 70:30% into training and test subsets respectively . Experimental results show that the generalization of the C5.0 DT has been increased radically using boosting, winnowing and tree pruning methods. The C5.0 DT model has achieved a remarkable performance with 98.95% classification accuracy on training subset and 100% of test one while RBF-SVM has achieved 100% success on both training and test subsets},