Fig. 8

Statistical analysis was performed by controlling demographic factors. Machine learning procedure was performed after removal of age and gender. ROC curves to predict NR and in-hospital mortality: A a RAN model presented a higher AUC for NR and; B in-hospital mortality prediction than all other models (NNET, SVM, and CTREE). Texture feature selection using the least absolute shrinkage and selection operator (Lasso) binary logistic regression model. The tuning parameter (λ) selection in the Lasso model was based on minimum criteria. C Lasso coefficient profiles of the 36 features for NR; D Lasso coefficient profiles of the 37 features for in-hospital mortality; coefficient profile plot was produced against the log (λ) sequence; E AUC curve was plotted versus log (λ) for NR; and F in-hospital mortality. Dotted vertical lines were drawn at the optimal values using the minimum criteria and one standard error of the minimum criteria. AUC, area under the curve; CTREE, Decision Tree; NNET, Neural Network Algorithm; RAN, random forest; SVM, Support Vector Machine