Nowadays, people are facing various health-related problems due to the modern life style what they follow. Breast Cancer is one of the most common problems among women worldwide which affects approximately 2.1 million women each year. Hence, it has become paramount to develop a system that can identify the major risk factors of Breast Cancer beforehand to make women aware about the risk factors and to take some precautionary measures to manage Breast Cancer. Consequently, this paper proposes a system called Transparent Breast Cancer Management System using P-Rules (TBCMS-PR) which identifies the major risk factors responsible for Breast Cancer in detail using decision tree and neural Network. TBCMS-PR uses decision tree to generate the rules for deciding the decision of Breast Cancer. Neural Network is used to keep only the relevant rules for Breast Cancer and to drop the irrelevant ones. Finally, the major risk factors with ranges are identified based on Sequential Search algorithm. The performance of the TBCMS-PR system is validated with the Breast Cancer dataset collected from UCI repository and is compared with a recent existing system. From the experimental results, it is observed that the proposed TBCMS-PR is significant and potential to manage Breast Cancer to a great extent by managing only one or two major risk factor(s).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.