A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.

TitleA Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.
Publication TypeJournal Article
Year of Publication2014
AuthorsGeng, Yuan, Wenbin Lu, and Hao Helen Zhang
JournalStat
Volume3
Issue1
Pagination337-350
Date Published2014
ISSN0038-9986
Abstract

Risk classification and survival probability prediction are two major goals in survival data analysis since 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 nonlinear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumor data and a breast cancer gene expression survival data are shown to illustrate the new methodology in real data analysis.

DOI10.1002/sta4.67
Alternate JournalStat
Original PublicationA model-free machine learning method for risk classification and survival probability prediction.
PubMed ID25530636
PubMed Central IDPMC4266578
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 CA140632 / CA / NCI NIH HHS / United States
Project: