Survival Support Vector Machines: A Simulation Study and Its Health-Related Application

Abstract

The support vector machine (SVM) has become a state-of-the-art classification method. Extensive developments of SVM ensure that support vector regression (SVR) is employed in many fields. In particular, in health applications, one of the most popular methods is survival data analysis. This paper describes the use of survival least square support vector machine (SURLS-SVM) applied to cervical cancer (CC) data with the benchmark Cox proportional hazards model (Cox PHM). The Cox PHM has assumptions that, unfortunately, cannot always be met in real cases. The SURLS-SVM overcomes this drawback. The SURLS-SVM cannot inform which predictors are significant, as the Cox PHM does. To address this issue, the feature selection using backward elimination is employed utilizing a concordance index increment. Moreover, the simulation study was conducted to know the effect of the number of features, sample size, and censoring percentage on the performance of the SURLS-SVM.