title: A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.
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.
authors.affiliation: Boehringer Ingelheim International Trading Co., Ltd., Shanghai, 200040, P. R. China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Geng
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
authors.forename: Wenbin
authors.initials: W
authors.lastname: Lu
authors.affiliation: Department of Mathematics, University of Arizona, Tucson, AZ 85721, USA.
authors.forename: Hao Helen
authors.initials: HH
authors.lastname: Zhang
keywords: Model-free; Risk classification; Support vector machines; Survival probability prediction