Work

A model‐free machine learning method for risk classification and survival probability prediction

Year: 2014

Type: article

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.

Source: Stat

DOI: 10.1002/sta4.67

Authors: Yuan Geng, Wenbin Lu, Hao Helen Zhang

Institutions: North Carolina State University, University of Arizona

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute