Figure 3. The receiver operating characteristic (ROC) curves and the decision curve analysis (DCA) for the patient-based risk scores and random forest models. (A) ROC curve for patient-based risk scores in the training set. (B) ROC curve for patient-based risk scores in the validation set. (C) ROC curve for patient-based random forest models in the training set. (D) ROC curve for patient-based random forest models in the validation set. (E) DCA for patient-based risk scores in the validation set. (F) DCA for patient-based random forest models in the validation set. In (E) and (F), the x-axis of the decision curve is the threshold of the predicted probability using the risk score to classify COVID-19 and non-COVID-19 patients. The y-axis shows the clinical decision net benefit for patients based on the classification result in this threshold. The decision curves of the treat-all scheme (the monotonically decreasing dash-line curve in the figure) and the treat-none scheme (the line when x equals zero) are used as references in the DCA. In this study, the treat-all scheme assumes that all the patients had COVID-19; the treat-none scheme assumes that none of the patients had COVID-19. Abbreviations: AUC, area under the ROC curve; 95% CI, 95% confidence interval.