## 6 Conclusions and Future Work  
Several different ML algorithms that can be used for ASD detection; however, some of them are unnecessarily time-consuming and prone to human error, and thus by the time the disease is detected, the patient may already be in the stage of ASD that is difficult to deal with. The challenge is to implement an automatic, fast, and accurate model for early ASD detection. This project aims to assess the ability of optimizing the wrapper FS method based on bio-inspired algorithms (GWO, FPA, BA, and ABC) to enhance the prediction accuracy of 16 ML models (GWO-NB, GWO-KNN, GWO-SVM, GWO-DT, FPA-NB, FPA-KNN, FPA-SVM, \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PBC** & \multicolumn{2}{c}{**GE**} \\ \cline{2-4}
**CNN** & **GWO-SVM** & **CNN** & **GWO-SVM** \\ \cline{2-4}
**Accuracy** & 98.64\% & 99.66\% & 99.98\% & 99.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison between the proposed models and the DL-based model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Previous Work [3]** & \multicolumn{4}{c}{**Proposed Models**} \\ \cline{2-6}  & **FA-SVM** & **GWO-SVM** & **FPA-SVM** & **BA-SVM** & **ABC-SVM** \\ \cline{2-6}
**Accuracy** & 97.95\% & 99.66\% & 99.5\% & 98.9\% & 98.63\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison between the proposed models and previous work [3] on the PBC dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Previous Work [27]** & \multicolumn{4}{c}{**Proposed Models**} \\ \cline{2-6}  & **DBN-IG** & **GWO-SVM** & **FPA-SVM** & **BA-SVM** & **ABC-SVM** \\ \cline{2-6}
**Accuracy** & 98.64\% & 99.34\% & 96.6\% & 97.4\% & 96.66\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison between the proposed models and previous work [27] on the GE dataset. FPA-DT, BA-NB, BA-KNN, BA-SVM, BA-DT, ABC-NB, ABC-KNN, ABC-SVM, and ABC-DT). The optimized wrapper FS methods were thus implemented with four different ML classifiers, NB, KNN, SVM, and DT. All of the algorithms were evaluated on two datasets and were compared with the results for the original classifiers. The experimental results showed the effectiveness of the proposed models in terms of the prediction accuracy of ASD, especially when we used the GE dataset. Generally, the models produced a good accuracy with both the PBC and GE datasets. Among all 16 models, GWO-SVM obtained the highest accuracy overall for both the PBC and GE datasets. In addition, the DL-based model achieved better accuracy results with big datasets such as GE rather than the PBC dataset. The main limitations faced in this work were the significant computation time when the number of features was large, as well as the large amount of memory and more powerful processor. In the future, the aim is to compare these algorithms based on bio-inspired algorithms with deep learning approaches for ASD prediction after obtaining more patient samples. Moreover, the combination between two dataset types with the same samples may provide more accurate results. Hybrid feature selection may also be used as a future approach, as it combines the advantages of both filter and wrapper algorithms. [PERSON] developed the model and performed the experiments. [PERSON] verified the analytical methods. All authors conceived the study and [PERSON] was in charge of overall direction and planning. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not Applicable. The two analyzed datasets during the current study are publicly published.