## 4 Results and Discussion
### Result Analysis
#### 4.1.2 Experiment 2 Results  
Experiment 2 aimed to assess the impact of three different FS techniques: RFECV, Boruta, and a GWO-based algorithm on the overall performance of the models. Each technique was evaluated individually to determine its effectiveness. RFECV and Boruta were applied after the random search hyperparameter tuning algorithm. This approach enabled the selection of optimal hyperparameters while considering the FS process. \begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Experiment 1.2: Models with Random Search**} \\ \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Feature Set/Number of Features**} & \multicolumn{2}{c}{**Model Performance on Training Dataset**} & \multicolumn{2}{c}{**ABIDE 1 + ABIDE 1**} & \multicolumn{2}{c}{**Prediction Performance on Testing Dataset**} & \multicolumn{2}{c}{**KAU**} \\  & & & **Mean Accuracy** & **Accuracy** & **Sensitivity** & **Specificity** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline CatBoost & F1: cortical region’s volumes/66 & 64.591 & 59.59 & 58.59 & 57.68 & 78.79 & 78.79 & 78.76 \\ \hline CatBoost & F2: volumes of sub-cortical regions/68 & 61.57 & 64.28 & 63.28 & 61.99 & 72.73 & 72.73 & 71.62 \\ \hline XGB & F3: surface area of sub-cortical regions/70 & 60.78 & 59.59 & 58.59 & 58.68 & 57.58 & 57.58 & 55.64 \\ \hline XGB & F4: cortical thickness regions & 63.53 & 60.38 & 59.38 & 58.95 & 66.67 & 66.67 & 63.53 \\ CatBoost & F5: mean curvatures of sub-cortical regions/68 & 63.92 & 62.72 & 61.72 & 58.91 & 84.85 & 84.85 & 82.14 \\ \hline CatBoost & F5: mean curvatures of sub-cortical regions/68 & 64.91 & 69.75 & 68.75 & 66.72 & 72.73 & 72.73 & 73.50 \\ \hline CatBoost & F6: all features/342 & 66.27 & 65.84 & 64.84 & 63.09 & 51.5 & 48.49 & 44.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experiment 1: performance results of the best baseline models (according to average cross-validation accuracy) for each feature set after using the random search technique. \begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Experiment 1.1 : Baseline Models**} \\ \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Feature Set/Number of Features**} & \multicolumn{2}{c}{**Model Performance on Training Dataset**} & \multicolumn{2}{c}{**Prediction Performance on Testing Dataset**} & \multicolumn{2}{c}{**KAU**} \\  & & & **Mean Accuracy** & **Accuracy** & **Sensitivity** & **Specificity** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline CatBoost & F1: cortical region’s volumes/66 & 62.94 & 58.81 & 57.81 & 56.76 & 63.64 & 63.64 & 61.84 \\ \hline SVM & F2: volumes of sub-cortical regions/68 & 61.18 & 61.94 & 60.94 & 60.46 & 69.7 & 69.7 & 67.11 \\ \hline CatBoost & F8: surface area of sub-cortical regions/70 & 59.61 & 62.72 & 61.72 & 60.39 & 54.55 & 54.55 & 53.01 \\ \hline CatBoost & F4: cortical thickness of sub-cortical regions/70 & 63.73 & 61.16 & 60.16 & 58.03 & 72.73 & 72.73 & 67.86 \\ \hline CatBoost & F5: mean curvatures of sub-cortical regions/68 & 64.12 & 69.75 & 68.75 & 67.47 & 63.64 & 63.64 & 62.78 \\ \hline RF & F6: all features/342 & 65.29 & 65.84 & 62.5 & 61.56 & 48.48 & 48.48 & 44.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experiment 1: performance results of the best baseline models (according to average cross-validation accuracy) for each feature set. On the other hand, the GWO-based algorithm encompassed both hyperparameter tuning and FS within a unified optimization framework, combining both aspects in separate steps. The results of Experiment 2 can be seen in Figure 10.  
In Experiment 2, RFECV resulted in mean validation accuracies ranging from 54.90% to 67.28%. The best accuracy (67.28%) was achieved by the DT model with 16 features from the F6 set (learning rate = 0.04, iterations = 250, depth = 6).