Figure 4.

Bootstrapped test-set ROC-AUC of all models trained to predict permanent new neurological deficit (pnND), sorted by mean ROC-AUC. QDA is the top-performing model, and LR represents the logistic regression baseline model (both highlighted). ROC-AUC = area under Receiver Operating Characteristic curve, QDA = quadratic discriminant analysis, LDA = linear discriminant analysis, KNN = k-nearest neighbors, GAM = generalized additive model, RF = Random Forest, XGB = extreme gradient boosting, ET = Extremely Randomized Trees, LR = logistic regression, SVM = support vector machine, MLP = multilayer perceptron.