In statistical prediction, the following three cross-validation methods are often used to examine a predictor for its effectiveness in practical application: independent dataset test, subsampling (such as 5-fold, 7-fold, or 10-fold cross-validation) test, and jackknife test [26] . In this study, the 5-fold cross-validation was employed to examine the performance of our method. The concrete procedures were that the training dataset were divided into five groups by splitting each of its subsets into five approximately equal-sized subgroups. Each of these five groups was in turn used as a testing dataset and the rest used as training dataset, thereby generating five different success rates, with their average representing the success rate by the 5-fold cross-validation.