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

Shweta Saxena, Manasi Gyanchandani

Abstract: 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.

Keywords: Breast Cancer, Computer-Aided-Diagnosis, Principal Component Analysis, Back-Propagation Neural Network

How to Cite?: Shweta Saxena, Manasi Gyanchandani, "A Model for Classification of Wisconsin Breast Cancer Datasets using Principal Component Analysis and Back-Propagation Neural Network", Volume 8 Issue 7, July 2019, International Journal of Science and Research (IJSR), Pages: 1324-1327, https://www.ijsr.net/getabstract.php?paperid=ART20199863, DOI: https://dx.doi.org/10.21275/ART20199863