Support vector methods for survival analysis: a comparison between ranking and regression approaches
Artificial Intelligence in Medicine
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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 survival data. SVRc leverages the high-dimensional capabilities of traditional SVR while adapting it for use with censored data through a modified asymmetric loss/penalty function which allows censored (left and right censored) data to be processed. We applied the new algorithm to predict the recurrence and disease progression of prostate cancer, breast cancer and lung cancer. Compared with the traditional Cox model, SVRc achieves significant improvement in overall accuracy as well as in the ability to identify high-risk and low-risk patient populations.