Ten-fold cross validation [48] is used in this work. The dataset is randomly divided into ten equal sets, out of which nine sets are used for training and the remaining one for testing. This procedure is repeated ten times and the final prediction result is the average accuracy of the ten testing sets. To reduce the computational time, we also adopt the independent testing dataset cross validation in this study as done by [49] to evaluate our model. Three parameters, sensitivity (S n ), specificity (S p ), and accuracy (Acc) are used to measure the performance of our model. They are defined by the following formulas: