Support Vector Regression for Censored Data (SVRc): A Novel Tool for Survival Analysis

Abstract

A crucial challenge in predictive modeling for survival analysis is managing censored observations in the data. The Cox proportional hazards model is the standard tool for the analysis of continuous censored survival data. We propose a novel machine learning algorithm, support vector regression for censored data (SVRc) for improved analysis of medical… (More)
DOI: 10.1109/ICDM.2008.50

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@article{Khan2008SupportVR, title={Support Vector Regression for Censored Data (SVRc): A Novel Tool for Survival Analysis}, author={Faisal M. Khan and Valentina Bayer Zubek}, journal={2008 Eighth IEEE International Conference on Data Mining}, year={2008}, pages={863-868} }