Breast Cancer Analysis Using Data Mining Techniques

Abstract

Cancer is an abnormal growth of cells in the body. These abnormal cells grow to form a swelling called tumor. It is the extra tissue growth in the breast which starts from a single abnormal cell that leads to the growth of a tumor. The most effective way to lessen the rate of death caused by breast cancer is early detection. Planning to help the physicians in refining the correctness of diagnostic results, PC supported analysis framework is helpful for disease recognition. This analysis was done by using some data mining techniques, such as biclustering mining, AdaBoost, and MapReduce, on the dataset of the patients. The MapReduce algorithm will perform mapping and reducing process to get a consolidated data from the enormous data. Then biclustering is a clustering algorithm, which is performed on the dataset and discovers a pattern based on similarities, which are used for further analysis process. Some of the nutrients are suggested for the patients to be taken and not to be taken during the period of treatment. The effective way for reducing the mortality of cancer and improving the healthy life of the affected patients is analyzing and getting awareness about the disease in the early stage. This proposed concept offers some of the easy and cost-effective ways for analyzing the curing possibility of cancer and the result will be provided very faster, which helps the patients to gain some confidence and satisfaction to overcome their disease.