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In this paper, we explore how the C4. 5 algorithm can be applied to breast cancer datasets in order to extract and formulate rules for identifying risk factors. For this study, we have used the Wisconsin dataset containing 9 attributes related to various cell features and anomalies. We have then applied the C4. 5 algorithm to that dataset to create a decision tree. From the inferred tree, the rules for identifying the patients at risk have been derived. With a training-set size of 200 patient records, our system was found to have an accuracy of 96.7%.