Additive survival least-squares support vector machines

This work studies a new survival modeling technique based on least‐squares support vector machines. We propose the use of a least‐squares support vector machine combining ranking and regression. The advantage of this kernel‐based model is threefold: (i) the problem formulation is convex and can be s...

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Bibliographic Details
Published in:Statistics in medicine 2010-01, Vol.29 (2), p.296-308
Main Authors: Van Belle, V., Pelckmans, K., Suykens, J. A. K., Van Huffel, S.
Format: Article
Language:English
Subjects:
Algorithms
Applied mathematics
Artificial Intelligence
Breast cancer
Breast Neoplasms - diagnosis
Breast Neoplasms - drug therapy
Breast Neoplasms - metabolism
Comparative analysis
Epidemiologic Research Design
Female
Humans
Kaplan-Meier Estimate
kernel-based learning
Least-Squares Analysis
LS-SVM
MATEMATIK
MATHEMATICS
Medical statistics
Neural Networks (Computer)
Oligonucleotide Array Sequence Analysis
Principal Component Analysis
Prognosis
Proportional Hazards Models
Recurrence
Risk
Survival Analysis
survival data
Tillämpad matematik
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Summary:This work studies a new survival modeling technique based on least‐squares support vector machines. We propose the use of a least‐squares support vector machine combining ranking and regression. The advantage of this kernel‐based model is threefold: (i) the problem formulation is convex and can be solved conveniently by a linear system; (ii) non‐linearity is introduced by using kernels, componentwise kernels in particular are useful to obtain interpretable results; and (iii) introduction of ranking constraints makes it possible to handle censored data. In an experimental setup, the model is used as a preprocessing step for the standard Cox proportional hazard regression by estimating the functional forms of the covariates. The proposed model was compared with different survival models from the literature on the clinical German Breast Cancer Study Group data and on the high‐dimensional Norway/Stanford Breast Cancer Data set. Copyright © 2009 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.3743