A model-free machine learning method for risk classification and survival probability prediction

Yuan Geng, Wenbin Lu, Hao Helen Zhang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Risk classification and survival probability prediction are two major goals in survival data analysis because they play an important role in patients' risk stratification, long-term diagnosis, and treatment selection. In this article, we propose a new model-free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data and is therefore capable of capturing non-linear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumour data and a breast cancer gene-expression survival data are shown to illustrate the new methodology in real data analysis.

Original languageEnglish (US)
Pages (from-to)337-350
Number of pages14
JournalStat
Volume3
Issue number1
DOIs
StatePublished - Mar 2014

Keywords

  • Model-free
  • Risk classification
  • Support vector machines
  • Survival probability prediction

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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