Fig. 2.

Violin plot of feature importance across cross-validation (CV) folds for aggregate subject-level analysis (left), and the site, which yielded the highest ROC-AUC (right). At each CV iteration, we extracted linear support vector machine (SVM) coefficients. The set of all coefficients from our SVM models are centered about 0. Deviation of coefficients from zero is an indication of the relative importance of individual features in the data. Features with positive and negative coefficients have positive and negative associations, respectively, with probability of classification as a case. The y axis lists variables for which SVM coefficients were strictly non-zero throughout all cross-validation iterations