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Cancer is one of the crucial causes of death for both men and women. All over the world, breast cancer is one of the leading cause of cancer deaths in women. The most effective way to reduce cancer death is to detect it earlier but the detection of cancer in early stages is not an easy process. As result, many researches are focused on developing different systems for breast cancer detection. In this paper we have discussed various data mining approaches that have been utilized for breast cancer diagnosis and prognosis. We have proposed a breast cancer prediction framework consisting of four main modules: Data Collection, Data Preprocessing, Feature Selection, and Classification. Evaluation results are provided as well. The goal is to find the best combination for feature extraction algorithm and classification algorithm, which will improve the accuracy of mammograms classification process.