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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

Country: India, Subject Area: Computers in Biology and Medicine

Pages: 1324 - 1327

Edition: Volume 8 Issue 7, July 2019

How to Cite this Article?

Shweta Saxena, Manasi Gyanchandani, "A Model for Classification of Wisconsin Breast Cancer Datasets using Principal Component Analysis and Back-Propagation Neural Network", International Journal of Science and Research (IJSR), https://www.ijsr.net/archive/v8i7/show_abstract.php?id=ART20199863, Volume 8 Issue 7, July 2019, 1324 - 1327

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