## 4 Results and Discussion
### Discussion  
This study explored ASD classification using seven models based on morphological features from various sMRI datasets of children aged 5-10 years. Different algorithms applied to the same data yielded a wide range of classification results. The baseline models demonstrated limited reliability and accuracy. However, the performance of tuned models improved, with a mean accuracy ranging from 52.55% to 66.28%. Notably, certain classifiers, such as NB and MLP, did not perform well, potentially due to insufficient training data relative to the number of dimensions, as well as variations arising from data sources, imaging techniques, and participant characteristics. Figure 11: Mean ASD classification accuracy of the models in Experiment 3. Models with a green frame indicate the highest accuracy, while models with a yellow frame indicate the best results among the remaining models. \begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Experiment 3: Models with GWO-Based Hyperparameter Tuning and Feature Selection Algorithms + Age and Gender**}} & \multicolumn{5}{c}{**Prediction Performance on Testing Dataset**} \\ \cline{3-10}  & \multirow{2}{*}{**Feature Set/Number of Features**} & \multicolumn{2}{c}{**Model Performance on Training Dataset**} & \multicolumn{5}{c}{**ABIDE 1: ABIDE II**} \\  & & **Mean Accuracy** & **Accuracy** & **Sensitivity** & **Specificity** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline SVM & & 70.19 & 60.5 & 62.5 & 60.31 & 69.67 & 69.67 & 67.12 \\ XGB & & 62.06 & 57.03 & 55.33 & 78.59 & 78.79 & 75.77 \\ AdaBoost & F5: mean curvatures of sub-cortical regions/13 & 69.80 & 62.72 & 61.72 & 60.14 & 63.64 & 67.97 & 66.77 \\ \hline SVM & & 71.1 & 64.28 & 61.28 & 61.74 & 57.58 & 57.58 & 53.76 \\ XGB & & 66.63 & 59.59 & 58.59 & 57.18 & 45.45 & 45.45 & 43.23 \\ AdaBoost & & 69.02 & 62.72 & 61.72 & 60.14 & 52.51 & 51.52 & 50.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experiment 3: performance results of the best optimized models (according to average cross-validation accuracy) that use selected features from the brain combined with age and gender information. Additionally, grid search exhaustively explores all combinations of hyperparameter values but is computationally expensive, while random search tries a subset of values to adjust the models [60]. However, both methods lack informative selection. In the recent experiment, nature-based methods efficiently searched for optimal hyperparameter values. The informative GWO algorithm, applied here for the first time in ASD studies, shows promise for future researchers to explore other sophisticated algorithms and enhance classification accuracy. The primary objective of this paper is not only to improve the accuracy of ASD classification but also to investigate the structural evidence associated with ASD. Recognizing that not all features carry equal importance, each feature contributes to the overall ASD classification score. In Experiment 2, various FS techniques, including RFECV, Boruta, and GWO-based algorithms, were employed to enhance model accuracy by reducing complexity, avoiding overfitting, and addressing the curse of dimensionality. Previous studies [16] without FS procedures reported low accuracy, especially with large, multi-source datasets containing redundancy and irrelevant data. By highlighting the major abnormal regions that house essential morphological features, the early diagnosis of ASD can be facilitated. The effectiveness of the features selected through FS techniques, particularly when multiple techniques agree on their significance and stability, demonstrates their value in improving ASD classification accuracy and providing insights into the underlying structural abnormalities associated with ASD. Figure 12 presents the number of features selected by the FS algorithms for each feature set.