A Model for Classification of Wisconsin Breast Cancer Datasets using Principal Component Analysis and Back-Propagation Neural Network

Nowadays, the second leading reason of death (due to cancer) among females is breast cancer. Early detection of this disease can significantly enhance the probabilities of long-term survival of breast cancer patients. This paper proposes a computer-aided-diagnosis model for Wisconsin Breast Cancer (WBC) datasets using Back-Propagation Neural Network (BPNN). The data pre-processing technique named principal component analysis (PCA) is proposed as a feature reduction and transformation method to improve the accuracy of BPNN.