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Los Angeles, CA
March 31, 2009 to April 2, 2009
ISBN: 978-0-7695-3507-4
pp: 28-32
ABSTRACT
Background: The diagnosis of cancer in most cases depends on a complex combination of clinical and histopathological data. Because of this complexity, there exists a significant amount of interest among clinical professionals and researchers regarding the efficient and accurate prediction of breast cancers. Results: In this paper, we develop a breast cancer prognosis predict system that can assist medical professionals in predicting breast cancer prognosis status based on the clinical data of patients. Our approaches include three steps. Firstly, we select genes based on statistics methodologies. Secondly, we develop three artificial neural network algorithms and four kernel functions of support vector machine for classifying breast cancers based on either clinical features or microarray gene expression data. The results are extremely good; both ANN and SVM have near perfect performance (99 – 100%) for either clinical or microarray data. Finally, we develop a user-friendly breast cancer prognosis predict (BCPP) system that generates prediction results using either support vector machine (SVM) or artificial neural network (ANN) techniques. Conclusions: Our approaches are effective in predicting the prognosis of a patient because of the very high accuracy of the results. The BCPP system developed in this study is a novel approach that can be used in the classification of breast cancer.
CITATION
Austin H. Chen, Guan-Ting Chen, Jen-Chieh Hsieh, Ching-Heng Lin, "BCPP: An Intelligent Prediction System of Breast Cancer Prognosis Using Microarray and Clinical Data", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 28-32, doi:10.1109/CSIE.2009.514
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