## 5 Experimental Results
### Experiment 5: Results Based on the Blood-Cell Dataset  
In this experiment, we evaluated the performance of the proposed FS optimizer in comparison with the other list of optimizers based on the Blood-Cell dataset. The results of this experiment are shown in Table 7. The resulting models with the extra FS step achieved better performance than the deep learning models without this step; see Table 3. As can be noticed in Table 7, the list of selected features from the proposed AHA-AO optimizer succeeded in achieving the best results in terms of both accuracy and time. In addition, the AHA-AO selected the smallest number of features compared to the other optimizers (347 (67.8%), 225 (43.9%), 226 (44.1%), 125 (24.1%), 132 (25.8%), 65 (12.7%)). The AHA-AO-based SVM achieved the best results compared to those of the other optimizers' classifiers (accuracy = 0.8862, BA = 0.8862, F1-score = 0.8878, recall = 0.8862, and precision = 0.9053), and its time was the best compared to those of the other SVM classifiers with other optimizers (0.2579, 0.53, 0.6661, 0.7249, 0.7944, and 0.8885 for AHA-AO, AHA, AO, WOA, MFO, and PSO). Regarding the time, the AHA-AO-based LDA achieved the best time compared to all other classifiers (0.1887), and its performance was comparable to that of the best-performing LDA of PSO (accuracy = 0.8826, BA = 0.8825, F1-score = 0.8844, recall = 0.8826, and precision = 0.903). The AHA-AO-based DT had the shortest time compared to the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Alg.**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**BA**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Time**} & \multicolumn{2}{c}{**No. of**} \\  & & & & & & & & & Features \\ \hline \multirow{3}{*}{PSO} & DT & 0.8013 & 0.7487 & 0.7875 & 0.8013 & 0.8177 & 0.3909 & \multirow{3}{*}{**79**} \\  & LDA & 0.8446 & 0.7953 & 0.8339 & 0.8446 & 0.87 & **0.1306** & \\  & SVM & 0.8478 & 0.8004 & 0.838 & 0.8478 & 0.8706 & 0.7182 & \\ \hline \multirow{3}{*}{MFO} & DT & 0.8141 & 0.7641 & 0.802 & 0.8141 & 0.8307 & 0.4391 & \multirow{3}{*}{91} \\  & LDA & 0.8462 & 0.7983 & 0.8361 & 0.8462 & 0.8694 & 0.1405 & \\  & SVM & 0.8574 & 0.8132 & 0.8492 & 0.8574 & 0.8774 & 0.7427 & \\ \hline \multirow{3}{*}{WOA} & DT & 0.8237 & 0.7778 & 0.8137 & 0.8237 & 0.8371 & 0.4166 & \multirow{3}{*}{98} \\  & LDA & 0.8397 & 0.788 & 0.8278 & 0.8397 & 0.8685 & 0.1709 & \\  & SVM & 0.8558 & 0.8103 & 0.847 & 0.8558 & 0.8778 & 0.6557 & \\ \hline \multirow{3}{*}{AO} & DT & 0.8189 & 0.7722 & 0.8085 & 0.8189 & 0.8321 & 0.419 & \multirow{3}{*}{91} \\  & LDA & 0.8446 & 0.7944 & 0.8336 & 0.8446 & 0.8718 & 0.1464 & \\ \cline{1-1}  & SVM & 0.8558 & 0.8111 & 0.8473 & 0.8558 & 0.8763 & 0.6305 & \\ \hline \multirow{3}{*}{AHA} & DT & 0.8061 & 0.756 & 0.7937 & 0.8061 & 0.8204 & 0.5743 & \multirow{3}{*}{99} \\  & LDA & 0.851 & 0.8038 & 0.8414 & 0.851 & 0.8744 & 0.1867 & \\ \cline{1-1}  & SVM & 0.8542 & 0.8081 & 0.8451 & 0.8542 & 0.8767 & 0.6737 & \\ \hline \multirow{3}{*}{AHA-AO} & DT & 0.8269 & 0.7812 & 0.8171 & 0.8269 & 0.8409 & 0.5981 & \multirow{3}{*}{96} \\ \cline{1-1}  & LDA & 0.8494 & 0.8017 & 0.8396 & 0.8494 & 0.8733 & 0.1783 & \\ \cline{1-1}  & SVM & **0.8686** & **0.8274** & **0.8617** & **0.8686** & **0.8869** & 0.6623 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification results of each feature selection optimization algorithm on the Chest-XRay dataset (bold refers to best value). other DT classifiers for all optimizers (0.4053). As a result, the features selected by the proposed AHA-AO model achieved the best performance with the SVM classifier and the best time when using the LDA. In addition, the AHA-AO-based SVM and DT classifiers achieved the best times compared to the SVMs and DTs of other optimizers, respectively. It can be noticed that the SVM classifier achieved better results compared to the DT and LDA for all optimizers.