## 5 Experimental Results
### Experiment 3: Results Based on the PH2 Dataset
With the AO feature set, the SVM showed a higher performance compared to that of DT and LDA (accuracy = 0.9643, BA = 0.9702, F1-score = 0.9643, recall = 0.9643, and precision = 0.9643). It is clear that the proposed hybrid algorithm improved the performance of both AHA and AO by either selecting fewer features or achieving better results. Of all of the optimizers, the best time complexity was achieved by the AHA-AO using the DT classifier (0.0482). In addition, the SVM based on the AHA-AO features had the best time complexity compared to that of all other SVM classifiers, and the LDA had the best time complexity compared to that of all other LDA classifiers. As a result, the proposed algorithm improved both the performance and the speed of all algorithms. The WOA feature set achieved the best results when used to train both the LDA and SVM (accuracy = 0.9679, BA = 0.9732, F1-score = 0.9679, recall = 0.9679, and precision = 0.9681), but the LDA took a longer time for training (0.1445) than the SVM did (0.0961). The MFO feature set achieved similar results to those of the WOA when using the SVM classifier (accuracy = 0.9643, BA = 0.9702, F1-score = 0.9643, recall = 0.9643, and precision = 0.9644), but the MFO-based SVM classifier was faster than the WOA-based SVM classifier. Although PSO selected the largest number of features, it showed similar results to those of the MFO and WOA optimizers when using both the LDA and the SVM classifier (accuracy = 0.9643, BA = 0.9702, F1-score = 0.9643, recall = 0.9643, and precision = 0.9648). We noticed that having a large number of features did not help the classifiers to fit the data well and achieve good results. This meant that many of the features selected using PSO, MFO, WOA, AO, and AHA added much noise in the resulting dataset without adding any variance in the data. On the other hand, the proposed optimizer selected the best 107 features, which added sufficient variance in the data, with the least noise. As a result, the resulting feature set achieved the highest results and, at the same time, the shortest time. As observed in Experiment 1, the DT classifier achieved the worst results with all optimizers. Its best results were achieved by using the AHA feature set (accuracy = 0.925, BA = 0.9256, F1-score = 0.9251, recall = 0.925, and precision = 0.9293), and it took the shortest time compared to the LDA and SVM based on the AHA features. In addition, DT achieved the worst results when using the WOA feature set (accuracy = 0.8786, BA = 0.869, F1-score = 0.8794, recall = 0.8786, and precision = 0.8991). The LDA achieved the best results with the WOA optimizer's features (accuracy = 0.9679, BA = 0.9732, F1-score = 0.9679, recall = 0.9679, and precision = 0.9681) and the worst results with the AHA-AO feature set (accuracy = 0.9571, BA = 0.9643, F1-score = 0.9571, recall = 0.9571, and precision = 0.9573). The SVM achieved the best results with the proposed AHA-AO algorithm and the worst results with the AHA features (accuracy = 0.9607, BA = 0.9673, F1-score = 0.9607, recall = 0.9607, and precision = 0.961). To sum up, Experiment 2 showed the superiority of the proposed AHA-AO optimizer compared to the other five optimizers with regard to either the performance or the time. The AHA-AO selected the best feature set (107 features) that had the least noise and the highest variance. Building a classifier based on this feature set is expected to be more preferable in real environments because it will be more interpretable, faster, and more accurate.