Support Vector Machines for Survival Analysis with R

This article introduces the R package survivalsvm, implementing support vector machines for survival analysis. Three approaches are available in the package: The regression approach takes censoring into account when formulating the inequality constraints of the support vector problem. In the ranking approach, the inequality constraints set the objective to maximize the concordance index for comparable pairs of observations. The hybrid approach combines the regression and ranking constraints in a single model. We describe survival support vector machines and their implementation, provide examples and compare the prediction performance with the Cox proportional hazards model, random survival forests and gradient boosting using several real datasets. On these datasets, survival support vector machines perform on par with the reference methods.

Césaire J. K. Fouodo , Inke R. König , Claus Weihs , Andreas Ziegler , Marvin N. Wright

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at

CRAN packages used

survivalsvm, kernlab, pracma, quadprog, Matrix, randomForestSRC, mboost, mlr, ggplot2, tikzDevice

CRAN Task Views implied by cited packages

MachineLearning, Multivariate, NumericalMathematics, Optimization, Survival, Cluster, DifferentialEquations, Econometrics, Graphics, HighPerformanceComputing, NaturalLanguageProcessing, Phylogenetics, ReproducibleResearch


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For attribution, please cite this work as

Fouodo, et al., "The R Journal: Support Vector Machines for Survival Analysis with R", The R Journal, 2018

BibTeX citation

  author = {Fouodo, Césaire J. K. and König, Inke R. and Weihs, Claus and Ziegler, Andreas and Wright, Marvin N.},
  title = {The R Journal: Support Vector Machines for Survival Analysis with R},
  journal = {The R Journal},
  year = {2018},
  note = {},
  doi = {10.32614/RJ-2018-005},
  volume = {10},
  issue = {1},
  issn = {2073-4859},
  pages = {412-423}