This work studies a new survival modeling technique based on least-squares support vector machines. We propose the use of a least-squares support vector machine combining ranking and regression. The advantage of this kernel-based model is threefold: (i) the problem formulation is convex and can be solved conveniently by a linear system; (ii) non-linearity is introduced by using kernels, componentwise kernels in particular are useful to obtain interpretable results; and (iii) introduction of ranking constraints makes it possible to handle censored data. In an experimental setup, the model is used as a preprocessing step for the standard Cox proportional hazard regression by estimating the functional forms of the covariates. The proposed model was compared with different survival models from the literature on the clinical German Breast Cancer Study Group data and on the high-dimensional Norway/Stanford Breast Cancer Data set.