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)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064


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Research Paper | Computers in Biology and Medicine | India | Volume 8 Issue 7, July 2019

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

Shweta Saxena, Manasi Gyanchandani

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

Edition: Volume 8 Issue 7, July 2019

Pages: 1324 - 1327

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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/search_index_results_paperid.php?id=ART20199863, Volume 8 Issue 7, July 2019, 1324 - 1327

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