Model evaluation and comparison
NB
We attempted to use several widely applied machine learning (ML) algorithms (NB, xgboost) for the construction of the prediction models.We used a receiver operating characteristic (ROC) curve, specificity, sensitivity, accuracy, PPV, NPV, and F1 score to evaluate the prediction model in both training and validation data. Before evaluation, optimal cutoffs were determined by maximizing the Youden index (i.e., sensitivity + specificity – 1) by the ROC curve in the validation set. In the validation set, ROC curves revealed that xgboost had the best predictive performances, with an area under the curve of 0.733, better than that of the area under the curves of NB (0.672) and LR (0.627) (Receiver Operating Characteristic (ROC) curve of model In the training set, the accuracy of the xgboost model (0.994) was higher than the accuracies of the other models. All details about the parameters of the models developed with different algorithms are shown in Model performance metrics.