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Title: Breast cancer prediction based on backpropagation algorithm
Authors: Azmi, M.S.B.M. 
Cob, Z.C. 
Issue Date: 2010
Abstract: Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. Currently there are three techniques to diagnose breast cancer: mammography, FNA (Fine Needle Aspirate) and surgical biopsy. In this paper, we develop a system that can classify "Breast Cancer Disease" tumor using neural network with Feed-forward Backpropagation Algorithm to classify the tumor from a symptom that causes the breast cancer disease. The main aim of research is to develop more cost-effective and easy-to-use systems for supporting clinicians. For the breast cancer tumor diagnosis problem, experimental results show that the concise models extracted from the network achieve high accuracy rate of on the training data set and on the test data set. Breast cancer tumor database used for this purpose is from the University of Wisconsin (UCI) Machine Learning Repository. ©2010 IEEE.
DOI: 10.1109/SCORED.2010.5703994
Appears in Collections:COE Scholarly Publication

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