We selected subsets of the most relevant mass clusters in the discovery cohort groups using the weighted Kernel-based Iterative Estimation of Relevance Algorithm [17] (wKIERA) that combines a stochastic-search estimation of distribution algorithm with a kernel pattern-recognition method. We then used discovered relevant subsets of mass clusters to build discriminatory predictive models. We adopted a supervised learning approach to derive a classification rule using the Support Vector Machine (SVM) method [18] . Briefly, we used 10-fold cross validation to select parameters for the SVM. For the final model parameters, we selected those that gave the overall highest accuracy across the whole 10-fold cross validation. To obtain robust accuracy estimates for the classifier on the discovery data, we took 100 random re-samplings of the data, using 80% for training and 20% for testing. We selected as a final classifier the one that produced the highest accuracy and was then tested on the validation cohort data. Results were expressed as sensitivity, specificity and accuracy (proportion of correct classifications) and plotted on Receiver Operator Characteristic (ROC) space plots.