## 4 Related Work in the ML and DL Domain
### Eye Tracking-Based Diagnosis of ASD  
The use of eye-tracking technology has provided evidence in favor of theories about the gaze habits of autistic youngsters. An effective way to evaluate ASD is by eye movement analysis, which can provide important information about a person's cognitive abilities, social communication skills, and visual attention. Eye-tracking technologies allow for the recording several types of gaze patterns, such as saccadic eye movements, blinking, and fixation [107]. By highlighting minute differences in gaze-related behaviors, it becomes possible to pinpoint characteristics that differentiate people with ASD from the rest. Technological obstacles, such as various equipment types and intricate analytical methods, hinder the collection and processing of eye tracking data. Cutting-edge AI techniques such as ML and DL are being applied to improve eye-tracking equipment for examining gaze patterns and attentional mechanisms in people diagnosed with and not diagnosed with ASD [62; 107]. Numerous research studies use AI algorithms to examine gaze patterns and diagnose autism. Through the analysis and classification of these gaze patterns, the integration of eye-tracking technology with AI algorithms has the potential for the early identification of ASD [62; 108; 109]. Some relevant works that have explored eye tracking to diagnose ASD are described below. [PERSON] et al. [110] categorized varying degrees of ASD risk using eye gaze data and demographic characteristics like age and gender. They developed feature descriptors incorporating these variables, and examining eye-gazing patterns verified their efficacy. Their work utilized the National Database for Autism Research (NDAR) \"Eye Tracking Subject-Experiment\" (ETS-E) dataset, which included 229 participants with varying degrees of ASD risk. PART, a deep NN, RF, and C4.5 DTs were among the classifiers put to the test. In addition to age and gender data, the dataset included information on eye gazing. By using age, gender, average fixation time, and raw eye gaze points, they created homogeneous feature vectors that enable precise ASD categorization determined by gaze patterns for in-depth analysis. A year later, using ML and visualization approaches, [PERSON] et al. [111] investigated the application of eye tracking for ASD screening. Fifty-nine school-age participants in the study watched age-appropriate pictures and videos while having their eye movements monitored. Eye-tracking scan paths were converted into visual pictures or representations, which were analyzed using a CNN. This approach aimed to ease the diagnostic task \begin{table}
\begin{tabular}{l l l l l l} \hline
**Citation** & **Dataset** & **Technique** & **Reported Metrics** & **Limitations** \\ \hline  & ABIDE dataset and & & & & \\  & Alzheimer’s Disease & & & & \\  & dataset, MRI Images of & Multi-instance & Accuracy: & 70.00\% & \\  & ASD, and non-ASD, & Conv-Transformer & Sensitivity: & 50.00\%, & Small dataset size, and limited \\  & Images of AD and & (LD-MILCT) & Specificity: & 69.00\%, & task generalization. \\  & Non-AD individuals: & & & F1-Score: & 33.00\% & \\  & Public & & & & \\ \hline  & ABIDE dataset, rs-fMRI & & Accuracy: & 74.00\% & Limited dataset size, limited \\  & Images of ASD and & GNN & Sensitivity: & 69.00\% & data preprocessing, and low \\  & non-ASD patients & & Specificity: & 74.00\% & accuracy. \\ \hline  & & Multiview & & & \\  & ABIDE dataset, rs-fMRI & & hyperedge-aware & Accuracy: & 78.54\% & \\  & Images of ASD and & & & & \\  & non-ASD patients & hypergraph, & Sensitivity: & 74.40\% & Imbalance dataset, overlooked \\  & Images of ASD and & & & Precision: & 82.00\% & demographic variables, and \\  & non-ASD patients & & & F1-Score: & 77.00\% & ignored temporal dynamics. \\  & & GNN & AUC: & 87.00\% & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._and achieve high classification accuracy. High classification accuracy was achieved by converting these eye-tracking scan paths into visual pictures and employing a CNN for analysis.