## 5 Evaluation and Findings
### Experimental Findings  
As mentioned above, 80% of each dataset were used for the training of the proposed model, while the remaining 20% of the data samples were kept unseen for the validation of the tuned model. More specifically, 61 and 54 data samples were used to test the proposed model and compare it with the other techniques using the Cleveland and Statlog datasets, respectively. The obtained confusion matrix by the proposed EHMFFL algorithm using both datasets can be seen in Figure 5.  
To find the effectiveness of the proposed ensemble EHMFFL algorithm against the base learners, a comparison of various performance measures using the test data samples from the Cleveland and Statlog datasets is provided in Tables 3 and 4, respectively. While some algorithms may display higher performance than the EHMFFL algorithm on a measure, the proposed method outperforms all the techniques on average for both datasets. Figures 5 and 6 show the accuracy of the EHMFFL algorithm using various methods. The EHMFFL algorithm surpasses all other methods, as illustrated in Figures 6 and 7.  
Figure 5: Confusion matrix of the proposed EHMFFL algorithm using the Cleveland and Statlog datasets.  
According to the different performance metrics for various classification techniques using the Cleveland dataset, the EHMFFL algorithm outperforms all the base learners with an accuracy of 91.8%, a precision of 91.4%, a recall of 94.1%, an F1 score of 92.8%, and a specificity of 88.9. This shows that the EHMFFL algorithm is the most effective and efficient method for the supplied dataset. Other algorithms also perform well in some cases, with accuracies ranging from 82% to 90.2%. When comparing the other algorithms, RF is the best base learner with an accuracy of 90.2% and, then, XGBoost and SVM with accuracies of 88.5% and 86.9% are in the next in order. Also, based on the results in Table 4, the EHMFFL algorithm exceeds all other algorithms with an accuracy score of 88.9%. After the EHMFFL algorithm, XGBoost, RF, and SVM, obtained better results than the other base learners with  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Algorithms** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1 Score** \\ \hline LR & 79.6 & 80 & 82.8 & 76 & 81.4 \\ DT & 81.5 & 85.2 & 79.3 & 84 & 82.1 \\ RF & 84.4 & 86.2 & 85.5 & 76 & 87.4 \\ NB & 77.8 & 79.3 & 79.3 & 76 & 79.3 \\ SVM & 83.3 & 81.3 & 89.7 & 76 & 85.2 \\ KNNs & 80.8 & 84.5 & 78.9 & 83 & 81.6 \\ XGBoost & 85.2 & 88.9 & 82.8 & 88 & 85.7 \\ EHMFFL (Proposed) & 88.9 & 92.6 & 86.2 & 92 & 89.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the EHMFFL algorithm with existing methods using the Statlog dataset.  
Figure 6: Comparison of the results of different methods using the Cleveland dataset in terms of precision, recall, and accuracy.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Algorithms** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1 Score** \\ \hline LR & 85.2 & 90.3 & 82.4 & 88.9 & 86.2 \\ DT & 82 & 84.8 & 82.4 & 81.5 & 83.6 \\ RF & 90.2 & 96.7 & 85.3 & 96.3 & 90.6 \\ NB & 85.2 & 87.9 & 85.3 & 85.2 & 86.6 \\ SVM & 86.9 & 88.2 & 88.2 & 85.2 & 88.2 \\ KNNs & 83.6 & 87.5 & 82.4 & 85.2 & 84.8 \\ XGBoost & 88.5 & 96.6 & 82.4 & 96.3 & 88.9 \\ EHMFFL (Proposed) & 91.8 & 91.4 & 94.1 & 88.9 & 92.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the EHMFFL algorithm with existing methods using the Cleveland dataset.  
accuracy scores of 85.2%, 84.4%, and 83.3%, indicating that these three methods are the most accurate base learners, the same as observed for the Cleveland dataset. The results show that the EHMFFL algorithm again shines out in terms of all the performance metrics, on average.