## 5 Discussion

Table 7: Classification report of model performance on CE dataset: (1) Synthetic + Real data: Transfer learning model trained on synthetic data. (2) Real data: Transfer learning model trained on real data. (3) No Transfer Learning: Deep learning model trained on real data without transfer learning. imbalance. This methodological choice ensures our model maintains a balance between predictive performance and practical applicability in diverse clinical settings. [PERSON] et al. [22] applied a cost-sensitive RUSBoost algorithm to identify high-risk hypertensive patients, achieving an F1 score of 93.47%. Similarly, [PERSON] et al. [46] used support vector machines (SVM) and reported an accuracy of 91.80% and an F1-score of 92.06%. These studies highlight the effectiveness of machine learning in cardiovascular risk prediction. Our inclusion of synthetic data generation and TL offers a unique advantage, by enhancing model adaptability and performance, particularly in scenarios with limited real-world data. Our study's results are also consistent with those of [PERSON] et al. [47], who proposed an enhanced deep convolutional neural network (EnDCNN) for cardiovascular disease prediction using electronic medical records (EMRs), achieving high accuracy and F-scores. This consistency across different methodologies and datasets underscores the robustness of deep learning techniques in HRV-based predictions. Additionally, their use of EMRs and our integration of synthetic data both reflect a broader trend towards leveraging diverse data sources to improve model performance. [PERSON] et al. [27] focused on distinguishing between healthy individuals and those with congestive heart failure using HRV data and machine learning algorithms. Their study \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Dataset** & **Method** & **Sensitivity** & **Specificity** & **Accuracy** & **F1** \\ \hline \multirow{3}{*}{[PERSON] et al. [20] (2015)} & - HRV for risk of vascular events & - Random Forest (RF) & 71.4\% & 87.8\% & 85.7\% & N/A \\  & - 139 Hotter recordings & & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [47]} & - EMR for cardiovascular disease (2019) & - Enhanced Character-level Deep CNNs (EnDCNNs) & N/A & N/A & 95.22\% & 95.16\% \\  & - 659 records & (EnDCNNs) & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [21] (2020)} & - HRV for cardio events & - Random Under-Sampling Boosting (RUSBOOST) & N/A & N/A & 97.08\% & 86.67\% \\  & - 139 Hotter recordings & (RUSBOOST) & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [22] (2021)} & - HRV for cardio events & - Cost-Sensitive RUSBoost (CS-RUSBoost) & N/A & N/A & 93.47\% \\  & - 139 Hotter recordings & (CS-RUSBoost) & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [46] (2023)} & - HRV for cardiovascular events & - Support Vector Machines (SVM) & N/A & 87.09\% & 91.8\% & 92.06\% \\  & - 139 Hotter recordings & (SVM) & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [27] (2024)} & - HRV for Heart Failure Prediction - 99 records & - Support Vector Machines (SVM) & 74\% & 74\% & 73\% \\ \cline{1-1}  & - HRV for Atrial & - Deep Learning with Synthetic Data & & & & \\ \hline \multirow{3}{*}{**Our CE Model** (2024)} & - HRV for Cardiovascular Events (2024) & - Transfer Learning Events - 139 Hotter records & - Transfer Learning with Synthetic Data & 83\% & 80\% & 82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of different studies on HRV analysis methods. achieved a highest accuracy of 74% using support vector machines. Our approach achieved a higher accuracy, and this difference may be owing to the combination of synthetic data and TL to address the limitations of the small dataset, a methodology not employed by [PERSON] et al. [27]. The incorporation of synthetic data generation and transfer learning in our study provides a robust framework for improving predictive accuracy in medical research, particularly for conditions with limited datasets.