Fig. 2.
Unimodal diagnostic accuracies for cognition (Cog), electrophysiology (EEG), structural magnetic resonance imaging (sMRI), and diffusion tensor imaging (DTI) for each of the nine different configurations of machine learning algorithms. X-axes show the accuracies (acc), and y-axes show the sum of correct classifications for each of the 100 random subsamples (see Fig. 1). Dotted vertical black line indicates chance accuracy (56%). With cognitive data, all nine configurations of algorithms significantly classified ‘patient v. control’ (p values = 0.001–0.009). No algorithms using EEG, sMRI, and DTI-data resulted in accuracies exceeding chance. The nine different configuration of machine learning algorithms: nB, naïve Bayes; LR, logistic regression without regularization; LR_r, logistic regression with regularization; SVM_l, support vector machine with linear kernel; SVM_h, SVM with heuristic parameters; SVM_o, SVM optimized through cross-validation; DT, decision tree; RF, random forest; AS, auto-sklearn. See text for details.