## 5 Simulation and Results of Cardiovascular Disease Prediction
### SHapley Additive exPlanations for Cardiovascular Disease Prediction  
SHAP is a technique that helps us to understand our model's outcome in more depth. SHAP is basically a visualization technique through which we can easily visualize the performance of our model. It explains the contribution of each feature to the prediction. SHAP sums up the behavior of each feature and decomposes the output. It calculates the value of each feature in the model and provides its outcome. SHAP is used to understand the importance of each feature and it is best for handling complex behaviors. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Models** & **Accuracy** & **F1 Score** & **Recall** & **Precision** &
\begin{tabular}{} **Execution Time** \\ **(s)** \\ \end{tabular} \\ \hline DNN & 0.815 & 0.815 & 0.815 & 0.814 & 742 \\ \hline VRNN & 0.838 & 0.850 & 0.921 & 0.789 & 2393 \\ \hline  & **0.910**, 0.914, & **0.909**, 0.913, & **0.909**, 0.914, & **0.912**, 0.920, & **1411**,1467, 1586, \\  & 0.920**, 0.922, & 0.920**, 0.922, & 0.920**, 0.922, & 0.925, 0.927, & 1527, 1466, 1405, \\ DVR-Net & 0.926**, 0.925, & 0.925, 0.925, & 0.926, 0.925, & 0.930, 0.929, & 1526, 1586, 1406, \\  & 0.927**, 0.925, & 0.926, 0.925, & 0.927, 0.925, & 0.930, 0.928, & 1504, 1408, **2853** \\  & 0.926**, 0.925, & 0.925, 0.925, & 0.926, 0.925, & 0.930, 0.926, & 1504, 1408, **2853** \\  & 0.924**, **0.906** & 0.924**, **0.904** & 0.924**, **0.904** & 0.923, **0.908** & \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Results of 10-FCV DVR-Net (30 epochs) for cardiovascular disease prediction. Figure 8: Confusion matrix of DVR-Net for cardiovascular disease prediction. Figure 9: ROC-AUC curve of DVR-Net for cardiovascular disease prediction. Determining the significance or contribution of each feature in DL is really challenging. SHAP values make each feature that contributes to the model's output visible. To forecast each feature's behavior for the prediction of CVD, we employed SHAP [48]. SHAP values are crucial for improving the interpretability of model outputs, particularly in complex models used for CVD predictions. By giving each feature a SHAP value we can determine how much of a contribution each feature makes to a particular prediction. Cooperative game theory is the source of SHAP values, which divide the prediction output among all features according to their respective impacts, thereby determining the role of each feature. This approach makes it easier to understand how various health indicators like heart rate, blood pressure, and cholesterol affect the final risk assessment for CVD. We applied SHAP explainer to view the internal working of our proposed model. SHAP explainer is used with kernel explainer with background data that is summarized. After the kernel explainer, we used all 22 features from our dataset to generate SHAP values. Using SHAP values, different visualization graphs are generated. **Force Plot:** SHAP force plots of DB-Net and DB-Net shown in Figures 10 and 11 illustrate individual predictions by demonstrating how each feature influences the model output to either move toward or away from a particular class in CVD prediction. The base value or average model output across the dataset is the focal point of each force plot. Red (positive SHAP values) indicates features that are pushing the prediction toward higher risk, while blue (negative SHAP values) indicates features that are pulling it toward lower risk. In both DB-Net and DVR-Net, for example, high blood pressure or high cholesterol may cause the prediction to lean toward a positive risk classification, whereas normal values for these characteristics may cause it to deviate from a risk indication. DVR-Net display more dynamic changes in SHAP values because of its focus on temporal or sequential patterns, while DB-Net's force plot display a more stable gradient because the DenseNet and DBN components handle individual feature importance differently.