## 4 Related Work in the ML and DL Domain
### Questionnaire-Based Diagnosis of ASD
After training and testing, the model could accurately predict sentiment on fresh input data, either 0 or 1. Conversely, [PERSON] et al. [73] used a dataset for adults with ASD and a children's dataset based on the AQ10 questionnaire with 292 samples of children aged four to eleven. The study determined the effectiveness of eight state-of-the-art classification methods in identifying ASD. The study evaluates five clustering algorithms in the absence of labeled data. It examines how well they perform using measures such as Silhouette Coefficient (SC), Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI). Based on ARI and NMI metrics, spectral clustering is the best performer, while \(k\)-means shows the greatest SC. ML algorithms were trained and evaluated using five-fold cross-validation to ensure robust results. We provide a summary of the studies related to the questionnaire data in Table 1. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Citation** & **Dataset** & **Technique** & **Reported Metrics** & **Limitations** \\ \hline \multirow{9}{*}{[64]} & Autism Screening & & & & \\  & Adult, Autistic & & & & \\  & Spectrum Disorder & & & & \\  & Screening Data for & & & & \\  & Children, Autistic & kNN, SVM, and RF & Accuracy: & 100.00\% & Limited feature exploration, generalizability concerns, and demographic constraints. \\  & (public), Spectrum Disorder Screening & & AUC: & 100.00\% & \\  & Data for Adolescence & & & & \\  & Data Set & & & & \\ \hline \multirow{9}{*}{[65]} & ASD Dataset of & Adaboost, FDA, C5.0, & & & \\  & toddler, child, adolescent, adult and & GImboost, LDA, MDA, PDA, SVM & Accuracy: & 98.36\% & Model generalization, and feature interpretation. \\  & ASDTest App: Public & and CART & & & \\ \hline \multirow{9}{*}{[66]} & ASD dataset for & & & & \\  & Children, Adult and & LR, SVM, NB, kNN, & Accuracy: & 99.53\% & Small dataset, feature interpretation, class \\  & Adolescent, Attributes based & ANN, CNN & Specificity: & 100.00\% & \\  & dataset & & & \\ \hline \multirow{9}{*}{[22]} & ASD Dataset, Autism & ANN, SVM, & & & \\  & detection in adults & Multi-Layer & Accuracy: & 100.00\% & Age and race bias, lack of dataset validation, and \\  & dataset (public) & Perceptron (MLP), CNN, RF, and LR & & 100.00\% & restricted generalization. \\ \hline \multirow{9}{*}{[67]} & Autism Screening & & & & \\  & dataset for toddlers (public) & SVM, RF, NB, LR, and kNN & Accuracy: & 97.15\% & \\  & & and kNN & F1-Score: & 98.00\% & Generalizability concerns. \\ \hline \multirow{9}{*}{[68]} & & NB, Baggage & & & \\  & ASD dataset toddlers, children, youths, and adults & Classifier, Random Tree, SVM, kNN, Classification and Regression & Accuracy: & 99.61\% & Data source bias, limited dataset diversity, lack of \\  & Austin Score & Classification and Regression & F1-Score: & 99.21\% & model comprehensibility, and Restricted clinical applicability. \\ \hline \multirow{9}{*}{[69]} & Autistic Spectrum & & & & \\  & Disorder Screening & ANN, SVM, DT, RF, & & & \\  & Data for adults & LR & Accuracy: & 99.40\% & \\  & & & & \\  & & & & \\ \hline \multirow{9}{*}{[70]} & Autism Screening & & & & \\  & dataset for toddlers, Autism screening on & & & & \\  & adults dataset, Autism screening & & & \\  & & & & \\  & & & & \\ \hline \multirow{9}{*}{[70]} & Autism screening & & & & \\  & dataset for toddlers, Autism screening on & & & \\  & & & & \\ \cline{1-1}  & adults dataset, Autism screening & & & \\ \cline{1-1}  & dataset, ASD dataset & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: A summary of works using AI techniques for detecting ASD through questionnaire data.