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.
- Risk classification
- Support vector machines
- Survival probability prediction
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty