On the use of a clinical kernel in survival analysis

Clinical datasets typically contain continuous, ordinal, cate- gorical and binary variables. To model this type of datasets, linear kernel methods are generally used. However, the linear kernel has some disad- vantages, 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.