Random forest (RF) classification and regression , eXtreme Gradient Boosting (XGBoost) , and deep neural network (DNN) were used for benchmarking machine learning algorithm. Area under the curve (AUC) of the receiver operating characteristic (ROC) curve measured the performance of the classification models. XGBoost learning rates were tuned to be 0.01. DNN consisted of a sequential model with three hidden layers. The main parameters were optimized sequentially as follows: learning rate=0.0001, optimizer=Adam, batch size=128, epochs=70. The number of neurons in hidden layers were tuned in a nested hyper parameter scheme with 700, 1000, and 2000 neurons in the first layer, 500 and 700 neurons in the second layer, and 200 and 300 neurons in the third layer.