## 4 Experiments and Discussion
### Results of Prediction Models and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Precision** & **Recall** & **F1-score** & **AUC** & **Accuracy** \\ \hline LR & 0.8789 & 0.8769 & 0.8775 & 0.9906 & 0.875 \\ RF & 0.8845 & 0.8804 & 0.882 & 0.991 & 0.88 \\ GBDT & 0.8858 & 0.8803 & 0.882 & 0.9914 & 0.88 \\ VGG16 & 0.8902 & 0.8909 & 0.8902 & 0.991 & 0.8875 \\ VGG19 & 0.9015 & 0.8946 & 0.8949 & 0.9897 & 0.8938 \\ ResNet50 & 0.9222 & 0.9156 & 0.9179 & **0.9933** & 0.9137 \\ ResNet101 & 0.8717 & 0.8725 & 0.8702 & 0.9879 & 0.87 \\ EfficientNetB0 & 0.8452 & 0.8367 & 0.8394 & 0.9822 & 0.835 \\ Proposed DCNN & **0.9381** & **0.9379** & **0.9376** & **0.9933** & **0.9375** \\ \hline \hline \end{tabular} Note: The best results are shown in bold.  
\end{table} 
Table 4: Overall performance comparison results of different prediction models. Figure 8: _Cont._  
In Figure 8a, it can be seen that the training and validation accuracy of the DCNN1 model increased suddenly during the first several epochs of training, and then gradually increased with increasing number of epochs; Figure 8b shows that the training loss of the DCNN1 model dropped rapidly during the initial stage and then declined slowly, while validation loss decreased gradually over all epochs. The accuracy and loss learning curves of the DCNN2 and DCNN4 models are similar to those of DCNN1, as shown in Figure 8c,d,g,h; nonetheless, the validation loss of DCNN2 in Figure 8c dropped quickly at first, and then declined rapidly over the following epochs. Figure 8e,f reveal the accuracy and loss learning curves of the DCNN3 model in training and validation. In Figure 8e, it can be seen that the training accuracy of the DCNN3 model increased slowly during the first stage of training, and then swiftly improved for several epochs and then increased steadily until the end; the validation accuracy suddenly decreased at the beginning and then rapidly increased over the next few training epochs, before increasing gradually. The behaviors of the loss learning curves in training and validation were the opposite of those  
Figure 8: The learning curves of the compared DL-based prediction models: (**a**) the accuracy learning curves of DCNN1; (**b**) the loss learning curves of DCNN1; (**c**) the accuracy learning curves of DCNN2; (**d**) the loss learning curves of DCNN2; (**e**) the accuracy learning curves of DCNN3; (**f**) the loss learning curves of DCNN3; (**g**) the accuracy learning curves of DCNN4; (**h**) the loss learning curves of DCNN4. of the accuracy learning curves during the training and validation, as shown in Figure 8f, showing that the training loss initially declined quickly, before decreasing steadily, while the validation loss increased in first epoch and then rapidly dropped before gradually declining with increasing numbers of epochs. Table 5 shows the results of the overall performance comparison of the DCNN prediction models with different structures like DCNN1, DCNN2, DCNN3, and DCNN4, with the results showing that the proposed DCNN-based method achieved the best performance with the highest values for precision, recall, F1-score, AUC, and accuracy, at 0.9381, 0.9379, 0.9376, 0.9933, and 0.9375, respectively, outperforming the other four models.