## 4 Results
### Feature Selection Results  
In this section, metaheuristic optimization algorithms are used to select the most important features from the large feature set obtained after feature fusion and to improve classification performance. GA, PSO, ABC and HHO algorithms were used for feature selection and the best method was determined. The experimental results are shown in Table 9. Experimental results show that the HHO algorithm provides the highest classification accuracy. Feature selection using HHO and SVM (sigmoid) model achieved 94.66% accuracy; this result shows that the selection made after feature fusion increases the  
Figure 8: Confusion matrix of SVM model with sigmoid kernel (0: Healthy, 1: Stage 1, 2: Stage 2, 3: Stage 3). \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Kappa** \\ \hline SVM (linear) & 88.77 & 88.82 & 88.77 & 88.72 & 0.8497 \\ SVM (polymon) & 91.14 & 91.16 & 91.14 & 91.14 & 0.8818 \\ SVM (off) & 90.11 & 90.16 & 90.11 & 90.05 & 0.8677 \\ SVM (sigmoid) & 92.00 & 91.93 & 92.00 & 91.91 & 0.8930 \\ RF & 87.97 & 87.98 & 87.97 & 87.91 & 0.839 \\ XGBoost & 88.60 & 88.52 & 88.59 & 88.62 & 0.8480 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance results of feature fusion stage on the test dataset. generalization ability of the model and significantly improves the classification accuracy. When the results obtained with other optimization algorithms are compared, GA achieved 93.23% accuracy, ABC algorithm achieved 93.72% accuracy and PSO achieved 93.23% accuracy. The higher accuracy of HHO indicates that it is the most appropriate feature selection strategy for HR classification. In general, feature selection with metaheuristic optimization algorithms improved classification success by removing redundant or low-impact features from the model. The HHO algorithm was determined to be the best method as it provided the highest accuracy and consistency and was used in the final model. The confusion matrix shown in Figure 9, obtained after feature selection using the HHO algorithm, demonstrates a significant improvement in classification performance compared to the SVM (sigmoid) model employed during the feature fusion stage. Especially in the 0 (Healthy) class, while 95 correct predictions were made in the feature fusion stage, this number increased to 96 with HHO and the number of incorrect predictions decreased. Similarly, in the 1 (Stage 1) class, while 80 correct predictions were made with feature fusion, this number increased to 86 with HHO and incorrect predictions decreased significantly. One of the areas where the model showed the greatest improvement was in the 2 (Stage 2) class. While 93 correct predictions were made in this class in the feature fusion phase, this value increased to 96 with HHO and the rate of incorrect predictions decreased. Especially in the feature fusion stage, the errors made between classes 1 and 2 were minimized by feature selection. In the Stage 3 class, 77 correct predictions were made with feature fusion and this value did not change with HHO, which shows that the model already classifies the most advanced stage diseases with high accuracy. In general, feature selection with the HHO algorithm provided better discrimination, especially in classes 1 and 2, and increased the overall accuracy of the model by reducing false predictions. Although using all features in the feature fusion stage improves the performance of the model, filtering out unnecessary features allows the model to generalize better and the distinction between classes becomes clearer. These results show that optimization with HHO is the best method for HR classification and the model provides higher accuracy.