A comparative summary of relevant work on cardiovascular disease prediction using machine learning and deep learning. The table compares methods, datasets, key performance metrics, and key findings. This review highlights the major issues in the area, such as the interpretability, the generalizability, the cost of computation, and the privacy of data, all of which motivate the proposed hybrid model.

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• Comparison of Sensitivity, Specificity, and Negative Likelihood Ratio with Deep Learning Models. Metrics are reported as point estimates with 95% CIs. This analysis extends the performance comparison to metrics paramount for clinical application. The proposed model achieves a competitive balance of these metrics against deep learning benchmarks. It attains the highest specificity (98.7%), minimizing false alarms, and a low Negative Likelihood Ratio (0.036), which is comparable to the best deep learning results. This demonstrates that the hybrid framework provides a highly reliable and efficient tool for clinical decision-making.
  by: Medhat A. Tawfeek (22522087)
  Published: (2025)
• Performance Comparison of the Proposed Hybrid Model with Deep Learning Models on the MIMIC-III test set. Metrics are reported as point estimates with 95% CIs. The proposed SVM-PSO-AI framework is evaluated against a Convolutional Neural Network (CNN) model, a combined Deep Learning/Machine Learning model, and a Deep Learning Neural Network. Reported metrics confirms the predictive power of the hybrid approach.
  by: Medhat A. Tawfeek (22522087)
  Published: (2025)
• P-value Analysis for statistical validation of the hybrid model against deep learning models. This analysis extends the validation in Table 9 to advanced deep learning architectures. It implies that even though the proposed hybrid SVM-PSO-AI model has a different structure, it is more powerful in predicting cardiovascular risk, and these findings cannot be explained by random change in the data.
  by: Medhat A. Tawfeek (22522087)
  Published: (2025)
• Statistical significance testing (P-value Analysis) of the performance improvements achieved by the proposed hybrid model over state-of-the-art machine learning models. A statistical comparison was done on all major performance indicators of each comparative model. The p-values which are obtained as a result of relevant statistical tests support the idea that the observed better performance of the hybrid model is statistically significant (p < 0.05 of all comparisons and most p-values < 0.005). This strict comparison confirms the fact that the improvement of the performance is not caused by mere coincidence but a direct consequence of the effectiveness of the proposed framework.
  by: Medhat A. Tawfeek (22522087)
  Published: (2025)
• Comparison of the proposed hybrid model with state of the art machine learning models on MIMIC-III test set. Performance metrics are reported as point estimates with 95% confidence intervals in parentheses. The proposed SVM-PSO- AI framework is compared to Logistic regression, K-Nearest Neighbors (KNN), a Decision Tree, which used Naive Bayes ensemble, and a Boosted Ensemble model. The comparison will be made using the standard classification metrics: Accuracy, Precision, Recall, F1-Score and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The findings illustrate that the proposed hybrid model has the best predictive capability in all the measures considered.
  by: Medhat A. Tawfeek (22522087)
  Published: (2025)