## 5 Simulation and Results of Cardiovascular Disease Prediction
### Validation of Deep Vanilla Recurrent Network for Cardiovascular Disease Prediction
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Precision**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Inference**} & \multirow{2}{*}{**Memory Usage (MB)**} & \multirow{2}{*}{**Epochs**} \\ \cline{1-1} \cline{5-8}  & & & & & & **Time (s)** & & **Time (s)** & \\ \hline DNN & 0.813 & 0.814 & 0.814 & 0.814 & 319 & 8 & 15 & 10 \\ VRNN & 0.808 & 0.830 & 0.940 & 0.793 & 829 & 20 & 15 & 10 \\ DVR-Net & 0.905 & 0.905 & 0.904 & 0.908 & 4428 & 20 & 25 & 10 \\ \hline DNN & 0.816 & 0.816 & 0.817 & 0.816 & 667 & 9 & 15 & 20 \\ VRNN & 0.838 & 0.844 & 0.898 & 0.798 & 1570 & 41 & 15 & 20 \\ DVR-Net & 0.904 & 0.904 & 0.904 & 0.904 & 5515 & 43 & 27 & 20 \\ \hline DNN & 0.815 & 0.815 & 0.815 & 0.814 & 931 & 12 & 15 & 30 \\ VRNN & 0.8383 & 0.850 & 0.92 & 0.789 & 1921 & 46 & 15 & 30 \\ DVR-Net & 0.906 & 0.904 & 0.904 & 0.908 & 8712 & 47 & 31 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of DVR-Net for cardiovascular disease prediction. \begin{table}
\begin{tabular}{l c} \hline \hline \multirow{2}{*}{DNN} & Dense layer (Number of neurons = 64, activation function = ‘linear’) \\  & Dense layer (num\_classes, activation function = ‘sigmoid’) \\ \hline \multirow{8}{*}{VRNN} & SimpleRNN (Number of neurons = 64, return\_sequences = True) \\  & SimpleRNN (Number of neurons = 64, return\_sequences = True) \\  & SimpleRNN (Number of neurons = 64, return\_sequences = True) \\  & SimpleRNN (Number of neurons = 64, return\_sequences = True) \\  & SimpleRNN (Number of neurons = 64, return\_sequences = True) \\  & Dropout (0.2) \\  & Dense layer (Number of neurons = 2, activation function = ‘sigmoid’) \\ \hline \multirow{4}{*}{DVR-Net} & Dense layer (Number of neurons = 64, activation function = ‘linear’) \\  & Dense layer (num\_classes, activation function = ‘sigmoid’) \\ \cline{1-1}  & SimpleRNN (Number of neurons = 64, return\_sequences = True) \\ \cline{1-1}  & Dropout (0.2) \\ \cline{1-1}  & Dense layer (Number of neurons = 2, activation function = ‘sigmoid’) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Deep learning models and DVR-Net architecture for cardiovascular disease prediction. Figure 7: Simulation representation of DVR-Net on different epochs for cardiovascular disease prediction. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Models** & **Accuracy** & **F1 Score** & **Recall** & **Precision** & **Execution Time (s)** \\ \hline DNN & 0.813 & 0.814 & 0.814 & 0.814 & 452 \\ \hline VRNN & 0.808 & 0.830 & 0.940 & 0.793 & 746 \\ \hline  & **0.893**, 0.911, & **0.893**, 0.911, & **0.893**, 0.911, & **0.893**, 0.912, & **805**, 804, \\  & 0.913, 0.915, & 0.912, 0.915, & 0.913, 0.915, & 0.915, 0.921, & 750, 805, \\ DVR-Net & 0.919, 0.920, & 0.918, 0.920, & 0.919, 0.920, & 0.922, 0.923, & 765, 765, 765, \\  & 0.917, 0.915, & 0.916, 0.915, & 0.917, 0.915, & 0.924, 0.922, & 804, 805, \\  & 0.920, 0.923, & 0.919, 0.923, & 0.920, 0.923, & 0.926, 0.926, & 804, 744, \\  & 0.911, **0.905** & 0.910, **0.905** & 0.915, **0.904** & 0.927, **0.908** & 805, **866** \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Results of 10-FCV DVR-Net (10 epochs) for cardiovascular disease prediction.