Analysis of classifier to improve Medical diagnosis for Breast Cancer Detection using Data Mining Techniques

Many research have been conducted to analyze Breast Cancer Data. Breast cancer is one of the leading cancers for women in developed countries including India. It is the second most common cause of cancer death in women. The high incidence of breast cancer in women has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is distinguish of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is to recur in patients that have had their cancers excised .In this work, we explore the applicability of association rule data mining technique to predict the presence of breast cancer. Also it analyzes the performance of conventional supervised learning algorithms viz. C5.0, ID3, APRIORI, C4.5 and Naive Bayes. Experimental results prove that C5.0 serves to be the best one with highest accuracy.