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Random Forest of Dipolar Trees for Survival Prediction

  • Małgorzata Krętowska
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)

Abstract

In the paper the method of using the ensemble of dipolar trees for survival prediction is presented. In the approach the random forest is applied to calculate the aggregated Kaplan-Meier survival function for a new patient. The induction of individual dipolar regression tree is based on minimization of a piece-wise linear criterion function. The algorithm allows using the information from censored observations for which the exact survival time is unknown. The Brier score is used to evaluate the prediction ability of the received model.

Keywords

Random Forest Primary Biliary Cirrhosis Median Survival Time Survival Function Primary Biliary Cirrhosis Patient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Małgorzata Krętowska
    • 1
  1. 1.Faculty of Computer ScienceBiałystok Technical UniversityBiałystokPoland

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