Receiver operating characteristic plots by randomly selected training (50%) and validation (50%) support vector machine (SVM) with optimal C search on overall study patients (N = 235). A semi-quantitative graphic presentation of the covariates relevance is presented for training and validation models. Full names of coded variables are reported in Additional File 2, Table S1. Keep = 1 means that covariate may stay in the model. Using 27 of 32 covariates, Gini's coefficient by training SVM was 1.00 and no false negative or false positive cases were identified among 118 randomly selected AAD Type A patients (error = 0%). However, validation SVM on the remaining 117 patients provided 15 false negative and 11 false positive cases (error = 22%) and the Gini's coefficient was 0.642 (ROC AUC 0.821), which is statistically lower (p < 0.01) than the results obtained by neural network model, shown in Figure 1. Of note that validation and training SVM use different covariates to predict outcome and a relatively different ranked importance. Nevertheless, with both training and validation SVM, apart from extracorporeal circulation time, the other 4 covariates were also selected by neural network models.