Detect Breast Cancer using Fuzzy C means Techniques in Wisconsin Prognostic Breast Cancer (WPBC) Data Sets

Medical data mining is very much valuable to medical experts. The main task of data mining is diagnosing the patient’s disease Classification. Breast cancer is a severe and life threatening disease very commonly found in woman. An unusual growth of cells in breast is the main source of breast cancer those cells can be of two types malignant (Cancerous) and benign (Non-Cancerous) these types must be diagnosed taking proper meditation and for proper treatment. Modern medical diagnosis scheme is totally based on data taken through clinical and/or other test; most of the decision related to classification of a disease is a very crucial and challenging job. In this research work, using intelligent techniques of data mining is Fuzzy C Means; we have focused on breast cancer diagnosis by fuzzy systems. Fuzzy rules are desirable because of their interpretability by human experts. It has been applied to classify data related to breast cancer from UCI repository site. Experimental works were done using MATLAB in order to reduce dimensionality of breast cancer data set a ranking based feature selection technique. Results on breast cancer diagnosis data set from UCI machine learning repository show that this approach would be capable of classifying cancer cases with high accuracy rate in addition to adequate interpretability of extracted rules. Keyword— Classification, Clustering, Fuzzy C Means, Breast Cancer, Wisconsin Prognostic Breast Cancer (WPBC).