|Title||A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Geng, Yuan, Wenbin Lu, and Hao Helen Zhang|
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.
|Original Publication||A model-free machine learning method for risk classification and survival probability prediction.|
|PubMed Central ID||PMC4266578|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA140632 / CA / NCI NIH HHS / United States
A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.