## 4 Results and Discussion
### Discussion
The GWO-SVM model, utilizing hyperparameter tuning and FS, achieved an accuracy of 64 on ABIDE test data. Empirically, 62 selected features out of 342 optimized the trade-off between the number of features and predictive power. Additionally, we assessed the models in terms of sensitivity and specificity across various sample features. We observed higher sensitivity than specificity across various sample features, consistent with [23] findings in younger age groups, indicating greater feasibility of ASD detection in those under 10 years old. Moreover, we note that the significant features for ASD classification varied among subsets. In our best model, GWO-SVM with F6, the frontal lobe and pars triangular regions, followed by the temporal lobe, parietal lobe, and to a lesser extent, the occipital lobe regions, along with age and gender features, played prominent roles. These regions are associated with movement, emotional and social behavior, memory, language, and eye-gaze direction perception [5; 14; 22; 47]. Figure 14 illustrates the roles of some of these brain regions. Comparison with Previous Methods  
To facilitate comparison with prior research, we focused on studies utilizing ABIDE I and ABIDE II datasets to diagnose ASD in children within our target age group. Table 9 presents accuracy-based comparisons. [PERSON] and [PERSON]'s papers are the only ones, to our knowledge, employing ML on both the ABIDE I and ABIDE II datasets for ASD classification [25; 26]. Figure 14: Some of the important regions of the brain, which are affected by autism, with their roles. Few differences between our models and previous studies exist. [PERSON] et al. [25] utilized a combined sMRI and fMRI dataset from 180 subjects aged 5-10 and employed a DBN + Logistic Regression model, achieving an accuracy of 65.56%. In contrast, our study focused exclusively on sMRI data from over 600 children, prioritizing clinical diagnostic effectiveness. Using conventional ML models, we achieved a higher accuracy of 71%, demonstrating that accurate predictions can be obtained without relying on complex and computationally expensive models. Additionally, while [PERSON] et al. utilized a single model with a DBN depth of 3 and a combination of rs-fMRI, GM, and WM features, we explored seven models with varying levels of statistical complexity. Our results revealed that utilizing structural features with SVM yielded superior accuracy compared to their approach. We also compared our study to [PERSON]'s [26], which proposed 14 models encompassing various network architectures, such as CNN, RNNs, spatial transformer networks, and CAM-based feature visualization. Despite the complexity, their maximum accuracy using 3D CNN for detecting ASD in the ABIDE datasets was 61.7%, lower than our results. Notably, our model outperformed 3D CNN by 9.7% in overall accuracy and avoided overfitting, which can occur when training complex DL models with limited datasets. Complex DL models run the risk of memorizing training data rather than acquiring meaningful representations. In contrast, ML models with simpler architectures tend to be less demanding in terms of data requirements and can still achieve satisfactory performance, even with smaller datasets. Moreover, in contrast to [PERSON]'s study [25], which utilized PCA for dimensionality reduction and resulted in new meaningless features, we employed advanced FS methods to select interpretable features. Our approach helped identify areas of damage and revealed important brain regions for ASD classification, which turned out to be partially consistent with the other studies discussed. Although significance may vary due to algorithm or sample composition differences, interpretability is crucial in neuroscience research for understanding biological processes and identifying meaningful biomarkers. ML models with hand-crafted features offer transparent insights into feature-outcome relationships.