Use of a machine learning algorithm for the selection of the optimal prediction model
REGRESSION
Four prediction models [logistic regression, support vector machine (SVM), boosting, and random forest] were established by machine learning. The SVM model had the lowest root mean square error (RMSE) (0.675), while the boosting and random forest models had RMSEs of 0.737 and 0.762, respectively; however, the logistic regression model was found to be unsuitable for RMSE evaluation. The area under the receiver operating characteristic curve (AUC) values were 0.823 for the logistic regression model, 0.841 for the SVM model, 0.827 for the boosting model, and 0.832 for the random forest model; the other predictors (accuracy, precision, and recall) are shown in Table The results of machine learning algorithm.AUC, area under the receiver operating characteristic curve; RMSE, root mean square error; SVM, support vector machine.