## 2 Related Work  
ML encompasses computational techniques using data for learning, performance improvement, and prediction. Although ASD classification methods can vary, Figure 1 illustrates the common steps involved in developing ML applications, including: (a) data collection and preprocessing; (b) feature extraction and selection; (c) model training; and (d) model testing and performance evaluation. Earlier studies on ASD categorization primarily focused on ML or deep learning (DL) techniques. [PERSON] et al. analyzed 45 articles proposing ML-based methods for ASD diagnosis using sMRI [5]. These studies differed in ML algorithms, hyperparameters values, sample size, and input features. There are very few publicly available datasets, with the ABIDE datasets being the most well-known. Typically, individual ML algorithms such as support vector machine (SVM) [12; 13], Naive Bayes (NB) [14], or k-nearest neighbor [15] were investigated in these studies. Ref. [16] used extreme gradient boosting (XGB) with ABIDE I male samples within a narrow age range, which limits generalizability to other age groups. Challenges increased when using the entire large dataset due to sample variability [17]. In the quest for biomarkers, various local and global parameters of the brain, such as white matter (WM) volumes and cortical thickness (CT), have been utilized [18; 19]. In [20], seven morphological features from 67 subjects were used, and the best accuracy was obtained using average CT for each of 68 regions of interest (ROIs). [PERSON] et al. [21] evaluated twenty ML approaches based on morphological brain networks (MBNs) by considering preprocessing steps, dimensionality reduction techniques, and learning methods. The top two teams achieved accuracies of 70% and 63.8% using the gradient boosting classifier (GBC). Several ML models have been used to distinguish autistic from TD participants using either sMRI alone [12] or sMRI combined with other data [22]. [PERSON] et al. [23] demonstrated the potential for early detection of ASD before the age of 10 using a random forest (RF) model on MRI features of male brains. Participants from the ABIDE I and II datasets were divided into four groups based on gender and health problems (TD or ASD) in [24] to investigate gender differences in ASD diagnosis. Following that, an ML model was trained using various combinations of functional and structural attributes. A DL model identified ASD with an accuracy of 65.6% in [25] using resting-state fMRI (rs-fMRI), white matter (WM), and gray matter (GM) features from the two ABIDE datasets. Unlike the early-life global volume increases in ASD, the volume of GM/WM varies in adult brain regions, increasing and decreasing. As a result, previous studies on ASD biomarkers yielded contradictory results. In [26], an end-to-end framework was offered which uses 14 models that are compounds of different network architectures, among them a static CNN model, sequential learning models (e.g., RNN), Spatial Transformer Networks, and feature visualization methods (such as CAM). For a private dataset, the authors discovered  
Figure 1: General machine learning procedures for ASD diagnosis. that the 2D3D CNN and RAM worked best, whereas for the ABIDE dataset, a basic 3D CNN performed best. The highest ACC of this study was 61.7%. Previous studies have shown promise in using ML algorithms for investigating ASD. However, these studies had limitations. First, many investigations trained a single model on small, high-dimensional MRI datasets, leading to overfitting and unstable models. Additionally, the generalizability of these models was questionable as they were not evaluated on external datasets [27]. Second, important biomarkers and neurophysiological significance of findings were not thoroughly analyzed in most models. Third, optimization techniques such as hyperparameter tuning and FS were often not employed, which can impact accuracy and efficiency.