Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining

Breast cancer is the most common cancer in women worldwide. Prevention of breast cancer through risk factors reduction is a significant concern to decrease its impact on the population. Attaining or detecting significant information in the form of rules is the key to prevent breast cancer. Our objective is to find hidden but important knowledge of the form of rules from the risk factors data set of breast cancer. Mining rules is one of the vital tasks of data mining as rules provide concise statement of potentially important information that is easily understood by end users. In this paper, we use association rule mining, a data mining technique to attain information in the form of rules from breast cancer risk factors data that could be useful to initiate prevention strategies. We discovered rules of both breast cancer and non-breast cancer patients so that we can understand and compare the characteristics of both breast cancer and non-breast cancer individuals. The experimental results show that generated or mined rules hold the highest confidence level.