A neural network based model for prognosis of early breast cancer

Applied Intelligence

Volumn 20, Issue 3, 2004, Pages 231-238

  Gómez Ruiz, Jose Antonio ^a   Jerez Aragonés, Jose M ^a   Muñoz Pérez, Jose ^a   Alba Conejo, Emilio ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

Backpropagation algorithm; Bayes error; Breast cancer; Medical prognosis; Multi layer feed forward neural networks

Indexed keywords

BACKPROPAGATION; DATABASE SYSTEMS; DECISION MAKING; GENETIC ALGORITHMS; MATHEMATICAL MODELS; PATHOLOGY; PATIENT MONITORING; PROBABILITY; REGRESSION ANALYSIS; TUMORS;

BACKPROPAGATION ALGORITHM; BAYES ERROR; BREAST CANCER; MEDICAL PROGNOSIS;

FEEDFORWARD NEURAL NETWORKS;

EID: 3543079195     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:APIN.0000021415.88365.c4     Document Type: Article

References (20)

1. W.L. McGuire, A.T. Tandom, D.C. Allred, G.C. Chamnes, and G.M. Clark, "How to use prognostic factors in axillary node-negative breast cancer patients," J. Natl. Cancer Inst., vol. 82, pp. 1006-1015, 1990.
2. R.P. German and T.J. Sejnowski, "Analysis of hidden units in a layered network trained to classify Sonar targets," Neural Networks, vol. 1, pp. 75-89, 1988.
3. Mc O'Neill, "Training back-propagation neural networks to define and detect DNA-binding sites," Nucleic Acids Res., vol. 19, pp. 313-318, 1991.
4. N. Qian and T.J. Sejnowski, "Predicting the secondary structure of globular proteins using neural network models," J. Mol. Biol., vol. 202, pp. 865-884, 1988.
5. H. White, "Learning in artificial neural networks: A statistical approach," Neural Computation, vol. 1, pp. 425-464, 1989.
6. W.G. Baxt, "Application of neural networks to clinicla medicine," Lancet, vol. 346, pp. 1135-1138, 1995.
7. P.M. Ravdin and G.M. Clark, "A practical application of neural network analysis for predicting outcome of individual breast cancer patients," Breast Cancer Research and Treatment, vol. 22, pp. 285-293, 1992.
8. M. Jefferson, N. Pendleton, B. Lucas, and M. Horan, "Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with nonsmall cell lung carcinoma," American Cancer Society, 1996.
9. S.A. Kaplan and P. Meier, "Nonparametric estimation from incomplete observations," J. Am. Stat. Assoc., vol. 53, pp 457-481, 1958.
10. D.R. Cox: "Regression models and life tables," J.R. Stat. Soc., vol. 34, pp. 187-220, 1972.
11. E. Alba et al., Estructura del patron de recurrencia en el cancer de mama operable (CMO) tras el tratamiento primario. Implicaciones acerca del conocimiento de la historia natural de la enfermedad. 7th Congreso de la Sociedad Española de Oncología Médica. Sitges, Barcelona. Abril 1999.
12. R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis, John Wiley and Sons: New York, 1973.
13. K. Funahashi, "Multilayer neural networks and Bayes decision theory," Neural Networks, vol. 11, pp. 209-213, 1998.
14. R. Rojas, "A short proof of the posterior probability property of classifier neural networks," Neural Computation, vol. 8, pp. 41-43, 1996.
15. G.E. Hinton, "Connectionist learning procedures," Technical report CMU-CS-87-115, Carnegie-Mellon University, Pittsburgh, PA, 1987.
16. R.M. Golden: Mathematical Methods for Neural Network Analysis and Design, MIT Press: Cambridge: MA, 1996.
17. S. Fahlma and C. Lebiere, "The Cascade Correlation Learning Architecture," Technical report CMU-CS-90-100, Carnegie-Mellon University, Pittsburgh, PA, 1990.
18. D.W. Patterson: Artificial Neural Networks, Theory and Applications, Prentice Hall, 1996.
19. S. Amari, N. Murata, K.R. Muller, M. Finke, and H. Yang, "Statistical theory of overtraining - Is cross-validation asymptotically effective?" Advances in Neural Information Processing Systems, vol. 8, pp. 176-182, 1996.
20. P. Janssen et al., "Model structure selection for multivariable systems by cross-validation," International Journal of Control, vol. 47, pp. 1737-1758, 1988.