## 3 Related Work
### ASD Prediction Using ML Approach  
#### 3.1.1 ASD Prediction Using ML without FS Methods  
[PERSON] et al. [18] tried to diagnose ASD by applying ML techniques on the personal and behavioral dataset. The k nearest neighbor (KNN), support vector machine (SVM), linear regression (LR), Naive Bayes (NB), and linear discriminant analysis (LDA) algorithms have used in the classification. The result of the implementation shows that the LDA algorithm had the best result of 72.2% and was the most accurate compared with the other algorithms. Likewise, [PERSON] et al. [5] developed an autism prediction model to classify ASD data. They used the KNN, SVM, and random forest (RF) ML classifiers. They performed their models for the clinical diagnosis of ASD of all ages on the basis of personal and behavioral characteristics. The results obtained indicate that the RF and SVM methods provided a high classification performance. Furthermore, [PERSON] et al. [19] focused on the development of some classification models using ML algorithms such as RF and LR algorithms, and the KNN algorithm with two datasets--adults and toddler. KNN has a higher accuracy score of 69.2% compared to the other two algorithms that are calculated in the experimental results which were 68% for LR and 67% for RF. [PERSON] et al. [20] used an existing dataset to implement a variety of ML methods. The aim was to test the accuracy of various approaches for abetter evaluation, and then to develop a model that would be used to predict children's autism. This was achieved by applying a standard autism test for infants, based on personal and behavioral assessments and widely used by psychologists and pediatricians to diagnose autism. The dataset contains 292 instances of children with 21 attributes. The RF and Support Vector Classifier (SVC) ML classifiers were applied, and the result was not satisfying--the highest accuracy was about 62%. This study by [PERSON] et al. [21] used a gene expression profile to predict ASD. In this study, they used the published microarray data (GSE26415) from the Gene Expression Omnibus database, which included 21 young adults with ASD and 21 unaffected controls. SVM, K-NN, and LDA classifiers were used to assess the predictive model. The highest performance was for SVM and KNN. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Type** & **Ref** & **ML Classifier** & **Classification Accuracy** \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & [27] & DBN & 98.64\% \\ \cline{2-4}  & [33] & CNN & 63\% \\ \cline{2-4}  & [34] & RNN & 70.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ASD prediction using DL. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & **Ref** & **FS** & **ML Classifier** & **Classification Accuracy** \\ \hline \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{[30]} & (TT)+ & \multirow{4}{*}{SVM} & \multirow{4}{*}{92.1\%} \\  & & (COR)+ & & \\  & & (WRS)+ & & \\  & & GBPSO & & \\ \cline{2-4}  & [31] & GA & RF & 87\% \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{[32]} & \multirow{4}{*}{PSO} & SVM & 81\% \\  & & & RF & 91\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._In addition, supervised ML techniques were used by [PERSON] et al. [22] to construct a model to diagnose ASD by classifying the genes that underlie this disease. To explore the results, they used SVM and DT. To validate the predictive results, a 10-fold cross-validation method was used. They found that, compared to SVM, the DT classifier performed better, with an accuracy of 94%. Similarly, [PERSON] et al. in [23] developed a machine learning-based methodology for the identification of some disease genes, including ASD. They applied different ML classifiers such as NB, SVM, and RF. The results show RF had the highest accuracy with 80%. The study by [PERSON] et al. [24] shows that brain markers can be used for identifying ASD.