Efficient support vector machine method for survival prediction with SEER data

Zhenqiu Liu; Dechang Chen; Guoliang Tian; Man Lai TANG; Ming Tan; Li Sheng

doi:10.1007/978-1-4419-5913-3_2

Efficient support vector machine method for survival prediction with SEER data

Zhenqiu Liu^*, Dechang Chen, Guoliang Tian, Man Lai TANG, Ming Tan, Li Sheng

^*Corresponding author for this work

Department of Mathematics

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

5 Citations (Scopus)

Abstract

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 penalized SVM method for mining right-censored survival data ( 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.

Original language	English
Title of host publication	Advances in Computational Biology
Editors	Hamid Arabnia
Pages	11-18
Number of pages	8
DOIs	https://doi.org/10.1007/978-1-4419-5913-3_2
Publication status	Published - 2010

Publication series

Name	Advances in Experimental Medicine and Biology
Volume	680
ISSN (Print)	0065-2598

Scopus Subject Areas

Biochemistry, Genetics and Molecular Biology(all)

User-Defined Keywords

Prognostic factors
SEER
Support vector machine
Survival analysis
SVM

Access to Document

10.1007/978-1-4419-5913-3_2

Cite this

@inproceedings{23bd3accadef4914a2722e256d83bd84,

title = "Efficient support vector machine method for survival prediction with SEER data",

abstract = "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 penalized SVM method for mining right-censored survival data ( 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.",

keywords = "Prognostic factors, SEER, Support vector machine, Survival analysis, SVM",

author = "Zhenqiu Liu and Dechang Chen and Guoliang Tian and TANG, {Man Lai} and Ming Tan and Li Sheng",

year = "2010",

doi = "10.1007/978-1-4419-5913-3_2",

language = "English",

isbn = "9781441959126",

series = "Advances in Experimental Medicine and Biology",

pages = "11--18",

editor = "Hamid Arabnia",

booktitle = "Advances in Computational Biology",

}

TY - GEN

T1 - Efficient support vector machine method for survival prediction with SEER data

AU - Liu, Zhenqiu

AU - Chen, Dechang

AU - Tian, Guoliang

AU - TANG, Man Lai

AU - Tan, Ming

AU - Sheng, Li

PY - 2010

Y1 - 2010

N2 - 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 penalized SVM method for mining right-censored survival data ( 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.

AB - 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 penalized SVM method for mining right-censored survival data ( 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.

KW - Prognostic factors

KW - SEER

KW - Support vector machine

KW - Survival analysis

KW - SVM

UR - http://www.scopus.com/inward/record.url?scp=79952199540&partnerID=8YFLogxK

U2 - 10.1007/978-1-4419-5913-3_2

DO - 10.1007/978-1-4419-5913-3_2

M3 - Conference proceeding

C2 - 20865481

AN - SCOPUS:79952199540

SN - 9781441959126

T3 - Advances in Experimental Medicine and Biology

SP - 11

EP - 18

BT - Advances in Computational Biology

A2 - Arabnia, Hamid

ER -

Efficient support vector machine method for survival prediction with SEER data

Abstract

Publication series

Scopus Subject Areas

User-Defined Keywords

Access to Document

Other files and links

Fingerprint

Cite this