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The Prognosis of Breast Cancer: A Comparison of Different Neural Network Architectures

Hazlina Hamdan, and NurAishah Mohd Taib, and Sameem Abdul Kareem, and Yip Cheng Har, (2004) The Prognosis of Breast Cancer: A Comparison of Different Neural Network Architectures. In: Proceedings of the Joint Conference on Informatics and Research on Women in ICT (RWICT) 2004 , 28 - 30 July 2004, Putra World Trade Center, Kuala Lumpur, Malaysia .

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Affiliations

University of Malaya

Abstract

Artificial neural networks are useful tools for solving many real-world problems and are usually utilized for complex data analysis. In the field of medicine, artificial neural networks have been used since the late 1980s, initially as an aid to diagnosis and treatment, and lately as a tool for the analysis of survival data. The main advantage of a neural network is its ability to generalise to new situations based on existing patterns. This advantage is used as a basis to compute and predict the survival of individual cases. This paper describes the research on the application of artificial neural networks in the prognosis of breast-cancer based on the cases seen in the University of Malaya Medical Centre from the year 1993 to 2002

Item Type:Conference or Workshop Item (Paper)
Keywords:Artificial neural network, back propagation, recurrent network, breast cancer, survival analysis, prognosis.
Subjects:Q Science
ID Code:1110

[IJ Street W. Nick 1999, A neural network model for Prognostic prediction. http://dollar.biz.uiowa.edu/u/~~street

[2] Burke, H.B., Goodman, RH., Rosen, D.B., Henson, D.E., Weinstein, J.N., , Harreil, RE., Marks, J.R., Winchester, D.R, and Bostwick, D.Q 1997. Artificial neural network improve the accuracy of cancer survival prediction. Cancer 79, 857-862.

[3] De Laurentis M. and Ravdin PM., 1994. A technique for using neural network analysis to perform survival analysis of censored data. Cancer Letters 15. no.77, 127-138.

[4] Ravdin PM, Clark GM., 1992, A practical application of neural network analysis for predicting outcome of individual breast cancer patients. Breast Cancer Research and Treatment 22,285-293.

[5] Abdul-Kareem Sameem, Sapiyan Baba, Yong Zulina Zubairi, U Prasad, Mohd Ibrahim A. Wahid, 2001, Back Propagation Neural Network for Medical Prognosis: A Comparison of Different Training Algorithms, EJSAT, Electronic Journal of School of Advanced Technologies, Vol.3 Issue'1, April 2001, Asian Institute of Technologies, Thailand.

[6] Abu Hanna A. and Lucas P.J.F, 2001, Prognostic model in medicine: AI and Statistical Approaches. Method of lnformation Science 40,1-5.

[7] Don McNeil, 1996. Epidemiological Research Methods, Wiley Series, New York.

[8] Charles H. Weaver and C. D. Buckner, what is cancer? http://www.cancerconsultants.com/patient/tnpv.php

[9] Harris J., M. Morrow and L. Norton, 1997. Principles and Practice of Oncology: Cancer of the Breast,. Phildelphia: Lippincott-Raven.

[10] Caudill, M and Butler, u, 1990. Naturally Intelligent Systems, MIT press, Cambridge.

[11] J F Dale Addison, Stefan Wermter, Kenneth J McGarry, John Maclntyre, 2003,Methods For Integrating Memory Into Neural Networks In Condition Monitoring. http://osiris.sunderland.ac.uk/~cs0kmc/357-044.pdf

[12] Berry, M. and Linoff, G, 1997, Data Mining Techniques. John Wiley, New York.

[ 13] Demuth, H. and Beale, M., 2000, Neural Network Toolbox User's Guide ver.4 For Use with Matlab. Massachusetts: The Math Works Inc.

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