Efficient support vector machine method for survival prediction with SEER data

Zhenqiu Liu, Dechang Chen, Guoliang Tian, Man Lai Tang, Ming Tan, Li Sheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 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 languageEnglish (US)
Title of host publicationAdvances in Computational Biology
EditorsHamid Arabnia
Pages11-18
Number of pages8
DOIs
StatePublished - Dec 1 2010

Publication series

NameAdvances in Experimental Medicine and Biology
Volume680
ISSN (Print)0065-2598

Fingerprint

Support vector machines
Computational complexity
Survival Analysis
Support Vector Machine
Experiments

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Liu, Z., Chen, D., Tian, G., Tang, M. L., Tan, M., & Sheng, L. (2010). Efficient support vector machine method for survival prediction with SEER data. In H. Arabnia (Ed.), Advances in Computational Biology (pp. 11-18). (Advances in Experimental Medicine and Biology; Vol. 680). https://doi.org/10.1007/978-1-4419-5913-3_2
Liu, Zhenqiu ; Chen, Dechang ; Tian, Guoliang ; Tang, Man Lai ; Tan, Ming ; Sheng, Li. / Efficient support vector machine method for survival prediction with SEER data. Advances in Computational Biology. editor / Hamid Arabnia. 2010. pp. 11-18 (Advances in Experimental Medicine and Biology).
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Liu, Z, Chen, D, Tian, G, Tang, ML, Tan, M & Sheng, L 2010, Efficient support vector machine method for survival prediction with SEER data. in H Arabnia (ed.), Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol. 680, pp. 11-18. https://doi.org/10.1007/978-1-4419-5913-3_2

Efficient support vector machine method for survival prediction with SEER data. / Liu, Zhenqiu; Chen, Dechang; Tian, Guoliang; Tang, Man Lai; Tan, Ming; Sheng, Li.

Advances in Computational Biology. ed. / Hamid Arabnia. 2010. p. 11-18 (Advances in Experimental Medicine and Biology; Vol. 680).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.

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Liu Z, Chen D, Tian G, Tang ML, Tan M, Sheng L. Efficient support vector machine method for survival prediction with SEER data. In Arabnia H, editor, Advances in Computational Biology. 2010. p. 11-18. (Advances in Experimental Medicine and Biology). https://doi.org/10.1007/978-1-4419-5913-3_2