## 4 Results and Discussion
### Result Analysis
#### 4.1.3 Experiment 3 Results  
In the final experiment, the optimal brain features identified in the top three models from Experiment 2 were combined with age and gender features. The hyperparameters of these models were then tuned using the GWO-based hyperparameter optimization \begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Feature SetNumber of Features**} & \multicolumn{3}{c}{**Model Performance on Training Dataset**} & \multicolumn{3}{c}{**Prediction Performance on Testing Dataset**} & \multicolumn{3}{c}{**KAU**} \\  & & **Mean Accuracy** & **Accuracy** & **Sensitivity** & **Specificity** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline \multicolumn{8}{c}{Experiment 2:1: models with random search, and recursive feature elimination with cross-validation (RFCV)} \\ \hline XGB & F1: cortical region’s volumes/12 & 66.08 & 59.59 & 58.99 & 57.43 & 63.64 & 63.64 & 63.72 \\ \hline MLP & F2: volumes of sub-cortical regions/17 & 65.49 & 64.28 & 64.78 & 61.74 & 69.7 & 69.7 & 67.11 \\ \hline DT & F3: surface area of sub-cortical regions/16 & 60.59 & 58.81 & 57.81 & 59.01 & 66.67 & 66.67 & 63.53 \\ XGB & F5: surface area of sub-cortical regions/16 & 60.58 & 55.69 & 54.69 & 53.05 & 57.88 & 57.58 & 57.52 \\ \hline XGB & F4: cortical thickness of sub-cortical regions/19 & 64.31 & 61.84 & 60.94 & 58.67 & 66.67 & 66.67 & 62.59 \\ \hline CatBoost & F5: mean curvatures of sub-cortical regions/29 & 65.69 & 65.84 & 64.84 & 62.84 & 69.7 & 69.7 & 68.05 \\ \hline DT & F6: all features/342 & 67.24 & 61.16 & 60.16 & 59.03 & 45.45 & 45.45 & 41.35 \\ \hline \multicolumn{8}{c}{Experiment 2:2 models with random search and Boruta} \\ \hline XGB & F1: cortical region’s volumes/6 & 64.90 & 54.91 & 53.91 & 51.88 & 60.61 & 60.61 & 63.91 \\ \hline SVM & F2: volumes of sub-cortical regions/4 & 64.71 & 61.94 & 60.94 & 60.21 & 66.67 & 66.67 & 67.91 \\ \hline XGB & F3: surface area of sub-cortical regions/3 & 62.35 & 56.47 & 56.48 & 54.98 & 48.48 & 48.48 & 48.68 \\ XGB & F4: cortical thickness of sub-cortical regions / 4 & 63.53 & 58.81 & 57.81 & 57.3 & 57.58 & 57.58 & 54.7 \\ \hline CatBoost & F5: mean curvatures of sub-cortical regions/4 & 63.73 & 58.81 & 57.81 & 56.91 & 66.67 & 66.67 & 61.65 \\ \hline Configut & F5: mean curvatures of sub-cortical regions/4 & 67.45 & 62.72 & 61.72 & 60.14 & 72.73 & 72.73 & 72.56 \\ \hline NN & \multirow{2}{*}{F6. all features/8} & \multirow{2}{*}{70.13} & \multirow{2}{*}{65.84} & \multirow{2}{*}{64.84} & \multirow{2}{*}{60.01} & \multirow{2}{*}{60.61} & \multirow{2}{*}{60.61} & \multirow{2}{*}{55.45} \\ SVM & & & & & & & & \\ \multicolumn{8}{c}{} & \multirow{2}{*}{70.68} & \multirow{2}{*}{59.38} & \multirow{2}{*}{58.56} & \multirow{2}{*}{54.55} & \multirow{2}{*}{52.08} \\ RF & & & & & & & \\ \hline \multicolumn{8}{c}{Experiment 2:3 models with random search and gray sub-based optimizer (GWO)} \\ \hline SVM & F1: cortical region’s volumes/16 & 67.06 & 64.91 & 64.94 & 59.46 & 54.58 & 54.55 & 49.25 \\ XGB & F1: cortical region’s volumes/16 & 67.26 & 55.69 & 54.69 & 53.80 & 60.61 & 60.61 & 68.72 \\ MLP & F2: volumes of sub-cortical regions/19 & 65 & 60.38 & 59.38 & 57.18 & 60.61 & 60.61 & 66.39 \\ \hline SVM & F3: surface area of sub-cortical regions/14 & 66.86 & 55.69 & 54.69 & 53.30 & 54.55 & 54.55 & 51.13 \\ \hline XGB & F4: cortical thickness of sub-cortical regions/19 & 63.51 & 60.38 & 59.38 & 58.95 & 66.67 & 66.67 & 63.53 \\ Celltoon & F4: cortical thickness of sub-cortical regions/19 & 66.63 & 61.16 & 60.16 & 58.03 & 66.67 & 66.67 & 62.59 \\ \hline SVM & F5: mean curvatures of sub-cortical regions/11 & 70 & 63.5 & 62.5 & 60.31 & 69.7 & 69.7 & 67.12 \\ \hline SVM & F6: all features/62 & 70 & 65.84 & 64.84 & 63.34 & 57.58 & 57.58 & 54.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experiment 2: performance results of the best optimized models (according to average cross-validation accuracy) for each feature set after using feature selection algorithms. algorithm.