Ensemble of dipolar neural networks in application to survival data

Abstract

In the paper the ensemble of dipolar neural networks (EDNN) for analysis of survival data is proposed. The tool is build on the base of the learning sets, which contain the data from clinical studies following patients response for a given treatment. Such datasets may contain incomplete (censored) information on patients failure times. The proposed method is able to cope with censored observations and as the result returns the aggregated Kaplan-Meier survival function. The prediction ability of the received tool as well as the significance of individual features is verified by the Brier score, and measures of predictive accuracy. © 2008 Springer-Verlag Berlin Heidelberg.