Breast Cancer Diagnosis and Prognosis are two medical applications which have posed a challenge to the researchers. The use of machine learning and data mining techniques has revolutionized the whole process of breast cancer Diagnosis and Prognosis. Breast Cancer Diagnosis distinguishes benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is likely to recur in patients that have had their cancers existed. Thus, these two problems are mainly in the scope of the classification problems. Most data mining methods which are commonly used in this domain are considered as classification category and applied prediction techniques assign patients to either a” benign” group that is non- cancerous or a” malignant” group that is cancerous. Hence, the breast cancer diagnostic problems are basically in the scope of the widely discussed classification problems. In this study, two powerful classification algorithms namely decision tree and Artificial Neural Network have been applied for breast cancer prediction. Experimental results show that the aforementioned algorithms has a promising results for this purpose with the overall prediction accuracy of 94% and 95.4%, respectively.