Survival analysis of censored data: neural network analysis detection of complex interactions between variables.

Abstract:

:Neural networks can be used as pattern recognition systems in complex data sets. We are exploring their utility in performing survival analysis to predict time to relapse or death. This technique has the potential to find easily some types of very complex interactions in data that would not be easily recognized by conventional statistical methods. In this paper we demonstrate that there are several ways neural networks can be used to find three-way interactions among variables. Thus, in data sets where such complex interactions exist, neural networks may find utility in detecting such interactions and in helping to produce predictive models.

authors

De Laurentiis M,Ravdin PM

doi

10.1007/BF00666212

subject

Has Abstract

pub_date

1994-01-01 00:00:00

pages

113-8

issue

1

eissn

0167-6806

issn

1573-7217

journal_volume

32

pub_type

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