A Data Mining Techniques for Diagnosis of Breast Cancer Disease

Breast cancer is more common problem among women in the last decade. The problem of classifying breast tumor has been an important issue for many years. Computer Aided Diagnosis (CAD) system assists in the detection and characterization of breast cancer. This paper investigates a number of data mining techniques in the detection of breast cancer. In machine learning, classifier ensembles have proven to be better than single classifiers. In this study, ensemble methods used for improving classifier performance to determine whether patients have breast cancer or not. Experiments have been conducted on Wisconsin Breast Cancer Dataset (WBCD). The results of the experiments are evaluated using metrics: Sensitivity, specificity and classification accuracy. The objective of this study was to improve the breast cancer detection with the application of data mining techniques.