Breast Cancer Detection using Data Mining Classification Methods

Abstract

Breast cancer can be happen to anyone, man and women. Breast cancer is an uncontrollable growth of breast cells. These cells form a tumor. There are  two types of tumor, cancerous (malignant) and non cancerous (benign). IARC fact sheet shows that breast cancer is the second causes of cancer death in more developed regions (198,000 deaths, 15.4%) after lung cancer. Clump thickness,  Uniformity of cell size, Uniformity of cell shape, Marginal Adhesion, Single Ephitelial Cell Size, Bare Nuclei, Bland Chromatin, Normal Nucleoli, Mitoses are attributes that been used to diagnose breast cancer. C4.5, Naive Bayes and k-Nearest Neighbor are data mining classification method that commonly used to detect desease. This paper presents comparison of data mining classification methods to detect breast cancer.