Abstract
Introduced is a neural network method to build survival time prediction models with censored and completed observations. The proposed method modifies the standard back-propagation neural network process so that the censored data can be used without alteration. On the other hand, existing neural network methods require alteration of censored data and suffer from the problem of scalability on the prediction output domain. Further, the modification of the censored observations distorts the data so that the final prediction outcomes may not be accurate. Preliminary validations show that the proposed neural network method is a viable method.
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References
De Laurentiis, M., Ravdin, P.M.: Survival analysis of censored data: Neural network analysis detection of complex interactions between variables. Breast Cancer Research Treatment 32, 113–118 (1994)
Jacob, V.S., Krishnan, R., Ryu, Y.U.: Internet content filtering using isotonic separation on content category ratings. ACM Transactions on Internet Technology 1(7), article 1 (2007)
Lapuerta, P., Azen, S.P., LaBree, L.: Use of neural networks in predicting the risk of coronary artery disease. Computers and Biomedical Research 28(1), 38–52 (1995)
Merz, C.J., Murphy, P.M.: UCI repository of machine learning databases. University of California, Irvine, Department of Information and Computer Sciences (1998)
Ohno-Machado, L.: Sequential use of neural networks for survival prediction in AIDS. In: Proceedings of American Medical Informatics Association Annual Fall Symposium, pp. 170–174 (1996)
Shin, C.K., Park, S.C.: Memory and neural network based expert system. Expert Systems with Applications 6, 145–155 (1999)
Shin, C.K., Yun, U.T., Kim, H.K., Park, S.C.: A hybrid approach of neural network and memory-based learning to data mining. IEEE Transactions on Neural Networks 11(3), 637–646 (2000)
Street, W.N.: A neural network model for prognostic prediction. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 540–546 (1998)
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Ryu, Y.U., Kim, J.K., Im, K.H., Hong, H. (2012). Neural Network Analysis of Right-Censored Observations for Occurrence Time Prediction. In: Shaw, M.J., Zhang, D., Yue, W.T. (eds) E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life. WEB 2011. Lecture Notes in Business Information Processing, vol 108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29873-8_10
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DOI: https://doi.org/10.1007/978-3-642-29873-8_10
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