## 5 Simulation and Results of Cardiovascular Disease Prediction
### Validation of Dense Belief Network for Cardiovascular Disease Prediction
High predictive accuracy is demonstrated by DB-Net, which exhibits a good balance between correctly classified positive and negative cases. This outcome shows that DB-Net is successfully identifying patterns in the data, leading to a low number of incorrect classifications. By concentrating on borderline samples, ProWRAS reduces FP and improves the precision and recall values of the model. The Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) curve for DB-Net is shown in Figure 6, demonstrating the model's capacity to discriminate between positive and negative classes at different threshold values. The strong model performance indicated by an AUC score near 1.0 reflects the high discriminative power of DB-Net in predicting CVD. Tables 8-10 show the results of 10-FCV for DB-Net with the individual models for different performance parameters against 30, 20, and 10 epochs. According to the results, it is validated that our DB-Net beats the base models with an accuracy of 91%. Figure 3: Simulation representation of DB-Net on different epochs for cardiovascular disease prediction. \begin{table}
\begin{tabular}{l c} \hline \hline
**Models** & **Architecture** \\ \hline \multirow{7}{*}{DBN} & Dense layer(Neurons = 512, activation function = ‘relu’) \\  & Dense layer (Neurons = 256, activation function = ‘relu’) \\  & Dense layer (Number of neuron = 128, activation function = ‘relu’) \\  & Dense layer (Neurons = 64, activation function = ‘relu’) \\  & Dense layer (Number of neurons = 32, activation function = ‘relu’) \\  & Dropout (0.2) \\  & Dense layer (Neurons = 1, activation function = ‘sigmoid’) \\ \hline \multirow{7}{*}{DenseNet} & Dense layer (Neurons = 256, activation function = ‘relu’) \\  & Dense layer (Neurons = 128, activation function = ‘relu’) \\  & Dense layer (Neurons = 64, activation function = ‘relu’) \\  & Dense layer (Neurons = 32, activation function = ‘relu’) \\  & Dropout (0.2) \\  & Dense layer (Neurons = 1, activation function = ‘sigmoid’) \\ \hline \multirow{7}{*}{DB-Net} & Dense layer (Number of neurons = 512, activation function = ‘relu’) \\  & Dense layer (Neurons = 256, activation function = ‘relu’) \\  & Dense layer (Neurons = 128, activation = function = ‘relu’) \\  & Dense layer (Neurons = 64, activation function = ‘relu’) \\  & Dense layer (Neurons = 32, activation function = ‘relu’) \\  & Dense layer (Neurons = 2, activation function = ‘sigmoid’) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Deep learning model and dense belief network architecture for cardiovascular disease prediction. \begin{table}
\begin{tabular}{l l} \hline \hline
**Common Parameters:** \\ \hline Loss Function & Binary cross\_entropy \\ \hline Metrics & Accuracy \\ \hline Epoch & 10–30 \\ \hline Batch Size & 32 \\ \hline
**DBN:** \\ \hline Hidden Layers & 5 \\ \hline Activation Function in every Hidden layer & sigmoid \\ \hline Optimizer & Adam \\ \hline Number of Neurons & 32–512 \\ \hline Activation Function on Output Layer & Sigmoid \\ \hline
**DenseNet:** \\ \hline Dense Blocks & 4 \\ \hline kernel\_regularizer rate & 0.001 \\ \hline Optimizer & Adam \\ \hline Dropout Rate & 0.2 \\ \hline Number of Neurons & 32–256 \\ \hline Activation Function in Dense Block & ReLU \\ \hline Activation Function on Output Neuron & softmax \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Hyperparameters utilized in DB-Net for cardiovascular disease prediction.