Hierarcffical neural networks for censored data

dc.contributor.author	Ai Wern, Ooi
dc.date.accessioned	2016-06-24T01:57:59Z
dc.date.available	2016-06-24T01:57:59Z
dc.date.issued	2007-06
dc.description.abstract	Although neural networks have been applied to survival analysis in recent years, their use in survival analysis has been limited, especially when in the presence of censored data. In this dissertation, a method for handling this problem is suggested. In particular, a hierarchical neural network is proposed. To train the effectiveness of this network. a data set consisting of 86 larynx cancer patients (Klein and Moeschberger, 1997) is used in the hierarchical neural networks and the nonhierarchical neural networks. Some error measurements are utilized to compare the effectiveness among the two models. The results show that the hierarchical model performs better than the nonhierarchical model.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/2197
dc.language.iso	en	en_US
dc.subject	Neural networks	en_US
dc.subject	Censored data	en_US
dc.title	Hierarcffical neural networks for censored data	en_US
dc.type	Thesis	en_US

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