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. 2018 Dec 18;49(16):2754–2763. doi: 10.1017/S0033291718003781

Fig. 2.

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.