On the use of a clinical kernel in survival analysis

Abstract. Clinical datasets typically contain continuous, ordinal, categorical and binary variables. To model this type of datasets, linear kernel methods are generally used. However, the linear kernel has some disadvantages, which were tackled by the introduction of a clinical one. This work shows that the use of a clinical kernel can improve the performance of support vector machine survival models. In addition, the polynomial kernel is adapted in the same way to obtain a clinical polynomial kernel. A comparison is made with other non-linear additive kernels on six different survival data. Our results indicate that the use of a clinical kernel is a simple way to obtain non-linear models for survival analysis, without the need to tune an extra parameter.