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A model‐free machine learning method for risk classification and survival probability prediction
Author(s) -
Geng Yuan,
Lu Wenbin,
Zhang Hao Helen
Publication year - 2014
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.67
Subject(s) - covariate , support vector machine , computer science , machine learning , artificial intelligence , model selection , parametric statistics , survival analysis , parametric model , data mining , statistics , mathematics
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. Copyright © 2014 John Wiley & Sons, Ltd.