Figure 2.

Outline of experimental design for MKL. (1) Types of different data used as inputs for (2) RBF kernels, which were then used for (3) fitting to training data and MKL. Kernels that are highly related to target outcomes are approximated with better accuracies. Combined approximations are used to (4) predict low vs. high disease activity in the validation and test set, and (5) calculate and evaluate performance measures for the Mklaren models.

EDSS: expanded disability status scale; CSF: cerebral spinal fluid; 1D: 1-dimensional; AUC: area under the curve; Clin: clinical data; IDP: image-derived phenotypes; MRI: cropped MRIs at the lateral ventricles; RBF: radial basis function; MKL: multiple kernel learning.