Efficient support vector machine method for survival prediction with SEER data.
Support vector machine (SVM) is a popular method for classification, but there are few methods that utilize SVM for survival analysis in the literature because of the computational complexity. In this paper, we develop a novel [Formula: see text] penalized SVM method for mining right-censored survival data ([Formula: see text] SVMSURV). Our proposed method can simultaneously identify survival-associated prognostic factors and predict survival outcomes. It is easy to understand and efficient to use especially when applied to large datasets. Our method has been examined through both simulation and real data, and its performance is very good with limited experiments.
Liu
Zhenqiu
Z
University of Maryland at Baltimore, Baltimore, MD, USA. zliu@umm.edu
Chen
Dechang
D
Tian
Guoliang
G
Tang
Man-Lai
ML
Tan
Ming
M
Sheng
Li
L
D001185:Artificial Intelligence; D019295:Computational Biology; D057225:Data Mining / Q000706:statistics & numerical data; D016208:Databases, Factual; D006801:Humans; D011379:Prognosis; D018426:SEER Program / Q000706:statistics & numerical data*; D016019:Survival Analysis