Deep neuron network (DNN) is a neural network with more than one hidden layer between the input and output layers. In DNN, thousands of neurons in each layer can be extensively applied to the dataset with thousands of features, and more advanced regularization technique such as the dropout can be used to prevent the overfitting problem (LeCun et al., 2015). Nevertheless, DNN requires the users to adjust a variety of parameters. The number of epochs, the size of mini-batches and the dropout rate are the most important parameters. The number of epochs refers to the number of times that the model is exposed to the training dataset. The size of mini-batches defines the number of training samples exposed to the model before updating of the weight. The dropout rate is the percentage of neurons that are randomly-selected and ignored during the training. In this study, the number of epochs (100, 200, 300, 400, 500, and 600), the size of mini-batches (60, 80, 100, 120, 140, and 160), and the dropout rate (0.1, 0.2, 0.3, 0.4, and 0.5) will be probed in the cross-validation. The dropout technique is exerted only after each hidden layer. Moreover, four configurations of deep neuron network with the different numbers of hidden layers (2 or 3 layers) and neurons per layer (1,024 or 2,048) are explored, which are defined in detail as follows: DNN2 (Figure S5) contains two hidden layers [input layer: X (1,024 or 2,048) neurons; hidden layer1: X neurons; hidden layer2: X neurons; output layer: 2 neurons], and DNN3 (Figure S6) includes three hidden layers [input layer: X (1,024 or 2,048) neurons; hidden layer1: X neurons; hidden layer2: X neurons; hidden layer3: X neurons; output layer: 2 neurons]. Additionally, the rectified linear unit function (ReLU) is used as the activation function. adam algorithm is adopted as the optimizer and “binary crossentropy” is employed for the loss function.