## 5 Popular Datasets for ASD Detection or Classification  
This section describes some popular ASD-related datasets relative to the various modalities available in the current literature. MRI datasetsThe ABIDE I and ABIDE II datasets are open-access and publicly available, consisting of resting state fMRI (r-fMRI) data samples or regional and total brain characteristics of the brain connectome. The ABIDE I dataset, launched in 2012, consists of a total of 1112 samples collected from over 17 various global sites, including 539 ASD and 573 typical control participants that are aged from 7 to 64 years. The ABIDE II launched in 2016, with contributions from 19 locations, provided a total of 1114 samples with enhanced phenotypical data, including 521 ASD people and 593 additional longitudinal samples from 38 people, who were participants aged 5 to 64 years. The major limitation of the ABIDE I dataset for ASD is the imbalanced distribution of gender (male and female). For instance, over 85% of participants are male, reporting a 4:1 male-to-female diagnosis ratio. This gender inequality makes it difficult for AI models to detect ASD accurately in females since girls may determine distinct brain patterns and behaviors. As a result, models instructed on \begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Citation** & **Dataset** & **Technique** & **Reported Metrics** & **Limitations** \\ \hline \multirow{3}{*}{[130]} & \multirow{3}{*}{IncRNA gene dataset} & NB, BN, kNN, LR, RF, linear and polynomial & Accuracy: & 78.31\% & \multirow{3}{*}{Customization.} \\  & & SVM & Sensitivity: & 90.02\% & \\  & & linear and polynomial & Specificity: & 66.50\% & \\  & & SVM & F1-Score: & 80.60\% & \\  & & MCC: & 58.30\% & \\ \hline \multirow{3}{*}{[131]} & ASD gut metagenomic & \multirow{3}{*}{RF} & Accuracy: & \multirow{3}{*}{97.00\%} & Lacking a comparative \\  & dataset (SRP182132) & & & & evaluation with other ML \\ \hline \multirow{3}{*}{[132]} & Gene Expression & RF, LMT, SMO, LR, SVM, IBCGA & Sensitivity: & 84.00\% & \\  & & SVM, IBCGA & Specificity: & 79.00\% & \\  & & AUC: & 84.00\% & \\ \hline \multirow{3}{*}{[133]} & Self-collected data from & \multirow{3}{*}{NB, kNN, DT, SVM, and GE dataset} & Accuracy: & 99.69\% & \multirow{3}{*}{Feature interpretation, small} \\  & ASD Tests application & & Precision: & 99.65\% & \\  & and GE dataset & & F1-Score: & 99.67\% & \\  & & & AUC: & 99.66\% & \\ \hline \multirow{3}{*}{[134]} & A whole-genome & \multirow{3}{*}{XGBoost, NB, NN, and  RF} & Accuracy: & \multirow{3}{*}{82.49\% } & \multirow{3}{*}{Limited diversity.} \\  & transcriptomic dataset & & RF & AUC: & \\ \cline{1-1}  & GSE6575, GSE28521 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A summary of works using AI techniques for detecting ASD by analyzing genetic data. Figure 3: A bar plot with the number of works in our review related to each modality. commonly male data may not behave well in real clinical settings where gender differences are more balanced [135]. Additionally, the dataset is taken from specific high-income countries, resulting in biases for similar human races or regions lacking diversity. Further, the data samples taken belong to humans of specific age groups, which reflects a need first to increase the data samples as they are quite low and require targeting a larger age group to improve the effectiveness of these datasets. Lastly, the quality of data samples can differ based on the imaging technology used by the institute from which it is taken and collectively can affect the preprocessing, impacting the model's performance upon training [38; 136]. **Questionnaire datasets**. The Autism Screening on Adults dataset, ASD children traits dataset, and Autism Screening data for toddlers datasets are all publicly available on Kaggle. All these datasets consist of ASD Questionnaire-based responses filled out by parents or caregivers of autistic individuals using the ASD Test application.