## 5 Discussion  
In this section, the results obtained at different stages of the proposed method are analyzed comparatively. First, the classification results using only CNN models are analyzed. DenseNet169, MobileNet and ResNet152 were identified as the three most successful models, with DenseNet169 having the highest accuracy rate. However, the classification performance of CNN models alone could not fully resolve the ambiguities between classes. First, 14 different CNN models were tested and the three highest performing models were identified. DenseNet169, MobileNet and ResNet152 models were used separately for classification and the accuracy rates were calculated as 87.73%, 86.40% and 85.87%, respectively. Although the DenseNet169 model had the highest accuracy rate, it was observed that the model could not adequately distinguish between different HR stages and made misclassifications at some stages. This indicates that approaches based on a single CNN model may be insufficient for HR diagnosis. Table 10 compares classification performance at different stages of the proposed method.  
In the feature fusion stage, the deep features of the three CNN models with the highest accuracy were combined and classified with different ML algorithms. The combined feature set was classified with SVM, RF and XGBost algorithms. The results showed that the SVM (sigmoid) model achieved the highest accuracy. At this stage, the accuracy rate increased to 92.00%, which is a significant improvement over methods using only individual CNN models. Feature fusion significantly improved the performance of the model compared to individual CNN models. In particular, the misclassifications between stages 1 and 2 of HR decreased and the generalization capacity of the model increased.  
In the third stage, feature selection was performed using GA, ABC, PSO and HHO to further improve the accuracy of the model. After feature selection, the highest accuracy rate of 94.66% was obtained using the HHO method. Through the optimization process, the complexity of the model was reduced, redundant features were filtered out and the overall classification accuracy was improved. Especially in classes 1 and 2, misclassifications decreased and the generalization ability of the model improved. In particular, the performance of the model improved by up to 7% in the middle and advanced stages of HR (stages 2 and 3). When the overall comparison is made, there is a significant improvement from the results obtained with CNN models alone to the feature fusion stage, and the greatest success is achieved after feature selection. This process shows that it is critical to use both DL and optimization techniques together to achieve the best performance in HR classification.  
As shown in Figure 10, accuracy increases from 87.73% with DenseNet169 to 92.00% (+4.27 points) after feature fusion and reaches 94.66% (+2.66 points) after HHO-based feature selection. Precision, recall and F1 score also show the same upward trend. This significant improvement highlights the cumulative benefit of the pooling and selection steps in improving classification performance.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Technique** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Kappa** \\ \hline DenseNet169 & 87.73 & 87.75 & 87.73 & 87.67 & 0.8359 \\ MobileNet & 86.40 & 86.60 & 86.40 & 86.31 & 0.8180 \\ ResNet152 & 85.87 & 86.01 & 85.87 & 85.83 & 0.8188 \\ Feature Fusion & 92.00 & 91.93 & 92.00 & 91.91 & 0.8930 \\ SVM (Linear) & 94.66 & 94.66 & 94.66 & 94.64 & 0.9286 \\ (HHO) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparative analysis of classification performance across different stages of the proposed method, including individual CNN models, feature fusion and metaheuristic-based feature selection.