## 5 Simulation and Results of Cardiovascular Disease Prediction
### Validation of Dense Belief Network for Cardiovascular Disease Prediction  
DB-Net outperform the state-of-the-art models with an accuracy, F1-score, precision, and recall of 92%, 92%, 92%, and 92%, respectively, as shown in Table 5. The combination of dense layers are used for their feature extraction capabilities and densely connected layers produces DB-Net's superior results. By directly using the outputs from the previous layers, the architecture, in particular the dense block structure, helps avoid the vanishing gradient issue, thus improving gradient flow and model stability during training. Furthermore, dense blocks minimizes parameter usage, increasing computational effectiveness without compromising performance. DenseNet and DBN the baseline models that perform poorly because they are not able to generalize across all features. While DenseNet alone is effective, it might not capture enough abstract patterns essential for the prediction of CVD. DBN, on the other hand, takes a lot of processing time and can face overfitting issues. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Models** & **Accuracy** & **F1-Score** & **Recall** & **Precision** & **Training** & **Inference** & **Memory Usage (MB)** & **Epochs** \\ \hline DBN & 0.844 & 0.844 & 0.846 & 0.841 & 833 & 20 & 13 & 10 \\ DenseNet & 0.865 & 0.865 & 0.865 & 0.865 & 826 & 20 & 13 & 10 \\ DB-Net & 0.905 & 0.910 & 0.948 & 0.875 & 850 & 20 & 23 & 10 \\ \hline DBN & 0.853 & 0.849 & 0.841 & 0.829 & 1802 & 20 & 13 & 20 \\ DenseNet & 0.864 & 0.861 & 0.865 & 0.881 & 1610 & 20 & 13 & 20 \\ DB-Net & 0.914 & 0.909 & 0.929 & 0.890 & 1705 & 20 & 28 & 20 \\ \hline DBN & 0.857 & 0.859 & 0.845 & 0.875 & 2377 & 20 & 13 & 30 \\ DenseNet & 0.861 & 0.856 & 0.828 & 0.885 & 2356 & 20 & 13 & 30 \\ DB-Net & 0.916 & 0.910 & 0.932 & 0.893 & 2756 & 20 & 30 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of DB-Net for cardiovascular disease prediction. The baseline models (DBN and DenseNet) have significantly lower training, inference time, and memory usage compared to DB-Net, despite its superior performance. DB-Net architecture integrates the outputs of dense blocks and consists of several densely connected layers. Since the outputs of each layer are directly connected to those of preceding layers, there are many parameters to store and control throughout training and inference. The baseline models, DBN and DenseNet, with fewer layers and consequently fewer parameters, use less memory. Because of its dual-process design and layered structure, DB-Net has a longer inference time and more memory usage than other models. Baseline models are faster during inference and have less memory usage because they employ a single architecture. The 'Adam' optimizer aids in the compilation of DB-Net, exhibiting a learning rate of 0.0001. The complete architecture utilized in base models and DB-Net is illustrated in Table 6. The training of the DB-Net model is performed using the training data. Table 7 shows the list of hyperparameters utilized in individual model and DB-Net model. According to the results, it is validated that our DB-Net beats the base models with an accuracy of 92%. Figure 3 shows the performance metrics of individual models and DB-Net for 10, 20, and 30 epochs. Figure 4 shows that the execution time of both DB-Net and DVR-Net is higher than the individual models. Higher execution times are the tradeoff for improved accuracy. For instance, DB-Net can take up to 2756 s to train over 30 epochs, while DenseNet and DBN only take about 2356 and 2377 s, respectively. Because dense layers and blocks are integrated to provide stable training and effective gradient flow, this results in increased computational load. The DB-Net confusion matrix, which shows the classification performance on the CVD prediction task, is shown in Figure 5. The matrix displays the quantity of False Positives (FP), False Negatives (FN), True Positives (TP), and True Negatives (TN).