White box radial basis function classifiers with component selection for clinical prediction models

This work proposes a new method to obtain flexible and sparse risk prediction models. The proposed method performs as well as a support vector machine using the standard RBF kernel, but has the additional advantage that the resulting model can be interpreted by experts in the application domain.