Detection of Breast Cancer using Data Mining Tool (WEKA)

Breast cancer has become the primary reason of death in women in developed countries. Breast cancer is the second most common cause of cancer death in women worldwide. The high incidence of breast cancer in women has increased significantly during the last few decades. In this paper we have discussed various data mining approaches that have been utilized for early detection of breast cancer. Breast Cancer Diagnosis is distinguishing of benign from malignant breast lumps. We have approached the diagnosis of this disease by using Data mining technique. Data mining is an essential step in the process of knowledge discovery in databases in which intelligent methods are applied in order to extract patterns. The most effective way to reduce breast cancer deaths is to detect it earlier. This paper discusses the early detection of breast cancer in three major steps of determining the breast cancer. They include (i) collection of data set, (ii) preprocess of the data set and (iii) classification. Data mining and machine learning depend on classification which is the most essential and important task. Many experiments are performed on medical datasets using multiple classifiers and feature selection techniques. A good amount of research on breast cancer datasets is found in literature. Many of them show good classification accuracy. For classification we have chosen J48.All experiments are conducted in WEKA data mining tool. Data-Sets are collected from online repositories which are of actual cancer patient Key WordsBreast Cancer, Data Mining, WEKA, J48 Decision Tree, ZeroR ——————————  ——————————