## 6 Cross-Dataset Evaluation for Cardiovascular Disease Prediction  
In DL, we usually train and test our model on the same dataset. However, it is critical to ensure the model's performance on unknown data. It is essential to ensure how well the model performs on training data and how well it generalizes on unseen data. DL models are often trained on a specific dataset to identify patterns and correlations within it. However, it is critical to guarantee that the model's performance extends beyond the training data and applies effectively to new data. Cross-dataset evaluation validates the model's robustness and applicability across many datasets.  
In our work, we evaluated our models on different datasets. For model training, we have used the HDHI, dataset and for testing, the CVD dataset is used. In 2021, the CDC provided CVD risk prediction data through the Behavioral Risk Factor Surveillance System (BRFSS) [49]. The CVD dataset displays all patient data in tabular form. This dataset contains 308,854 samples and 19 features. The BRFSS dataset is first preprocessed and cleaned. In preprocessing, two columns have been renamed, i.e., Weight_(kg) as weight and Height_(cm) as height. The majority of the dataset columns are categorical. Dealing with categorical values is challenging for DL models. Therefore, we have formatted them so that DL models can easily handle them. On the other hand, the training dataset HDHI contains 253,680 samples and 22 features. Using a different dataset to evaluate a model raises a number of issues. The testing dataset may differ from the training dataset in terms of feature representation, data distribution, and class distribution. To avoid this issue, we have removed three less important features, i.e., sex, fruits, and education, from the training dataset. The training dataset is then split into 80% training dataset and 20% testing dataset. DB-Net and DVR-Net are trained on the training dataset. Then, the CVD dataset is imported from the google drive and is first preprocessed by factorizing the categorical columns. The CVD dataset is then split into training and testing data. Cross-dataset  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Balancing Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Execution Time (s)** \\ \hline DBN & & 0.7720 & 0.7493 & 0.8166 & 0.7815 & 295 \\ DenseNet & ROSE & 0.7660 & 0.7190 & 0.8700 & 0.7870 & 168 \\ DB-Net & & 0.7760 & 0.7506 & 0.8256 & 0.7863 & 279 \\ \hline DBN & & 0.8330 & 0.8120 & 0.8370 & 0.8440 & 245 \\ DenseNet & SMOTE & 0.8000 & 0.7990 & 0.8220 & 0.7850 & 177 \\ DB-Net & & 0.8320 & 0.8570 & 0.8440 & 0.8510 & 279 \\ \hline VRNN & & 0.7270 & 0.6590 & 0.9370 & 0.7740 & 175 \\ DNN & ROSE & 0.7700 & 0.7700 & 0.7710 & 0.7690 & 44 \\ DVR-Net & & 0.7710 & 0.7779 & 0.7710 & 0.7700 & 231 \\ \hline VRNN & & 0.7930 & 0.7310 & 0.9250 & 0.8170 & 158 \\ DNN & SMOTE & 0.8110 & 0.8130 & 0.8120 & 0.8110 & 62 \\ DVR-Net & & 0.8160 & 0.8200 & 0.8160 & 0.8150 & 170 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Ablation results for DB-Net and DVR-Net for cardiovascular disease prediction using ROSE and SMOTE balancing.  
evaluation results on DB-Net and DVR-Net are shown in Table 19. The results indicate that the models generalize well to unseen data and maintain good performance. From the results, it is clear that there is no difference in results when the model is trained and tested on different dataset.