# Autism Screening in Toddlers and Adults Using Deep Learning and Fair AI Techniques
## 4 Results and Observations
### Comparative Analysis
* Suspected outliers can be dropped if they are caused by human error or data processing errors; \begin{table}
\begin{tabular}{c c c c} \hline
**Author and Year** & **Study/Analysis** & **Methodology/Parameters** & **Results** \\ \hline [PERSON] et al., 2022 [PERSON] et al. (2022) & \begin{tabular}{c} Early-stage detection \\ of ASD \\ \end{tabular} & \begin{tabular}{c} ML algorithms, SVM, \\ SHAP method \\ \end{tabular} & Accuracy is 96.82\% \\ \hline [PERSON] and [PERSON], 2023 [PERSON] and [PERSON] (2023) & \begin{tabular}{c} Detecting P300 signal \\ in autism \\ \end{tabular} & SMOTE, SVM & Accuracy is 91.12\% \\ \hline [PERSON] et al., 2023 [PERSON] et al. (2023) & \begin{tabular}{c} ASD detection using \\ transfer learning \\ \end{tabular} & \begin{tabular}{c} VGG 19, Inception V3, and \\ DenseNet 201 \\ \end{tabular} & Accuracy is 85\% \\ \hline [PERSON] et al., 2022 [PERSON] et al., 2022 [PERSON] et al. (2022) & ASD detection in children & \begin{tabular}{c} Dense Attentive GAN \\ (Generative adversarial network) \\ \end{tabular} & Accuracy is 97.82\% \\ \hline [PERSON] et al., 2023 [PERSON] et al. (2023) & \begin{tabular}{c} ASD detection using \\ facial diagnosis \\ \end{tabular} & \begin{tabular}{c} Vision Transformer, Gaussian \\ Process Layer \\ \end{tabular} & Accuracy is 94.50\% \\ \hline [PERSON] et al., 2022 [PERSON] et al. (2022) & \begin{tabular}{c} ASD classification using \\ clinical data \\ \end{tabular} & \begin{tabular}{c} Spatial–temporal Transformer \\ (ST-Transformer) \\ \end{tabular} & Accuracies are 71\% and 70.6\% \\ \hline [PERSON] and [PERSON], 2022 [PERSON] and [PERSON] (2022) & \begin{tabular}{c} Identifying autism \\ in children \\ \end{tabular} & CNN, Transfer Learning & \begin{tabular}{c} The Area Under Curve Score \\ is 96.63\% \\ \end{tabular} \\ \hline \multirow{3}{*}{**Proposed Work**} & \begin{tabular}{c} **Detecting ASD in** \\ **toddlers and adults using** \\ **ML, optimization, and** \\ **Fair AI** \\ \end{tabular} & \
\begin{tabular}{c} **Proposed CNN-LSTM-PSO**, \\ **GRU-CNN** \\ **Fairness techniques** \\ \end{tabular} &
\begin{tabular}{c} **Accuracies are 99.64\% and** \\ **98.99\%, respectively** \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._Similarly, properly formatted and validated data leads to improved data quality. It is also essential for protecting applications from inconsistencies like null values, duplicates, incorrect indexing, and incompatible formats, thereby eliminating bias through transformation. * Moreover, scaling the features normalizes the range of values of the independent variables. Standardizing data with different scales can eliminate inconsistencies and bias in measuring the same data characteristics. * SMOTE has been deployed to handle the data imbalance problem for both the datasets considered for this study;
* Finally, we see that optimization yields better accuracy and overall performance of the suggested models. As the performance of the models improves with the techniques suggested, the observed data points seem much closer to the predicted value, thereby reducing the error and improving the overall accuracy. Hence, it is correct to assume that the bias elimination techniques proposed in this study can indeed reduce the deviation. This results in a much more robust and efficient learning of the deep neural networks proposed.