To reduce the overfitting further, we used Ridge (L2) regularization method for adding penalties during updating weights. As the optimizer, we employed the adaptive moment estimation (Adam) optimizer with a 0.0001 learning rate. The loss function and the metric of the DNN model were the binary cross-entropy for the training. Moreover, 500 epochs and 1500 batch sizes were included.