# Heart Disease Prediction Using Concatenated Hybrid Ensemble Classifiers
## 4 Experimental Analysis
### Comparison with Existing Works  
In this subsection, we present a comparison of the performance of the proposed Concatenated Hybrid Ensemble Classifier with existing works in the field of using ML algorithms for heart disease prediction. Table 6 provides a comprehensive overview of accuracy, sensitivity, specificity, precision, and F1 Score, allowing readers to assess the efficacy of our model in comparison to other relevant studies. The comparisons are based on methodologies, key observations, and performance metrics reported in related works. The proposed model's performance metrics are also highlighted to provide a clear understanding of its superiority.  
The comparison with existing works reveals several noteworthy observations. The proposed Concatenated Hybrid Ensemble Classifier consistently outperforms other models in terms of accuracy, achieving an impressive 86.89%. This accuracy surpasses the reported averages of several existing models, indicating the robustness and efficacy of our approach. Additionally, the proposed model demonstrates competitive performance in terms of F1 Score (84.3%), showcasing a balanced trade-off between precision and recall. This is crucial for applications like heart disease prediction, where both false positives and false negatives have significant implications. A deeper analysis reveals that the strength of our model lies in its ensemble strategy, which combines the strengths of diverse classifiers such as SVM, Decision Tree, KNN, Logistic Regression, AdaBoost, and Naive Bayes. This amalgamation  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Existing Works** & **Methodology** & **Highest Accuracy** & **Highest F1 Score** \\ \hline
[9] & Logistic Regression, Naive Bayes, Random Forest, Decision Tree & Average Accuracy: 85\% & N/A \\ \hline
[10] & Naive Bayes, Decision Tree, KNN, Random Forest & Average Accuracy: 84\% & N/A \\ \hline
[13] & Logistic Regression & 84.53\% & N/A \\ \hline
[15] & Semi-Supervised Self-Training & 81.89\% & 87.14\% \\ \hline
[20] & SVM, Gaussian Naive Bayes, Logistic Regression, LightGBM, XGBoost, Random Forest & Average Accuracy: 80\% & N/A \\ \hline
[21] & Support Vector Machine, Decision Tree, Random Forest, Naive Bayes, Logistic Regression & N/A & N/A \\ \hline
[27] & Bagging Mechanism with KNN, Naive Bayes, Logistic Regression & Average Accuracy: 82\% & N/A \\ \hline
[28] & Random Forest, Decision Tree, Logistic Regression, SVM, KNN & Average Accuracy: 75\% & Highest AUC-ROC: 0.8675 \\ \hline Proposed Model & Concatenated Hybrid Ensemble Classifier & 86.89\% & 84.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with existing works.  
contributes to improved generalization and adaptability, surpassing models that rely on specific algorithms or ensemble techniques.  
Despite these strengths, it is essential to acknowledge potential limitations. Our model may not excel in scenarios in which specific algorithms dominate, and further investigation into these scenarios could guide future enhancements. Practically, the superior performance of our model holds promise for accurate and reliable heart disease prediction. The combination of feature selection and data standardization enhances its predictive power and contributes to its effectiveness.