# Autism Screening in Toddlers and Adults Using Deep Learning and Fair AI Techniques
## 4 Results and Observations
### Comparative Analysis  
In this section, the comparative analysis of the proposed work is presented with some previous related works. Table 5 summarizes the overall comparison with results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ML Models** & **Accuracy** & **Precision** & **Recall** & **F-1 Score** & **Execution** \\  & & & & **Time (ms)** \\ \hline KNN & 90.08 & 0.78 & 0.77 & 0.82 & 266,848 \\ LR & 93.05 & 0.90 & 0.84 & 0.88 & 208,664 \\ RF & 94.68 & 0.91 & 0.88 & 0.88 & 382,677 \\ DT & 92.62 & 0.89 & 0.82 & 0.84 & 300,116 \\ SVC & 92.36 & 0.90 & 0.88 & 0.90 & 480,248 \\ MLP & 95.04 & 0.92 & 0.82 & 0.86 & 521,436 \\ ANN & 95.83 & 0.89 & 0.89 & 0.88 & 588,644 \\ CNN & 96.81 & 0.92 & 0.93 & 0.91 & 682,042 \\ LSTM & 93.44 & 0.85 & 0.81 & 0.83 & 654.883 \\ GRU & 96.04 & 0.90 & 0.92 & 0.94 & 778,899 \\ CNN-LSTM & 96.66 & 0.92 & 0.91 & 0.94 & 729,890 \\ CNN-LSTM-PSO & 98.89 & 0.94 & 0.91 & 0.93 & 900,048 \\ GRU–CNN & 94.02 & 0.90 & 0.90 & 0.88 & 814,766 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of ML models (ASD in adults). Figure 13: Performance evaluation of ML models for ASD (Adults). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author and Year** & **Study/Analysis** & **Methodology/Parameters** & **Results** \\ \hline \multirow{4}{*}{[PERSON] et al., 2022 [1]} & Eye-tracking-based & \multirow{4}{*}{FFN, ANN, CNN, GoogleNet, ResNet-18} & Accuracy achieved, 95.5\% and \\  & & & \\  & & & \\ \cline{1-1}  & & & 94.5\% \\ \cline{1-1}  & & & \\ \hline \multirow{2}{*}{[PERSON] et al., 2022 [9]} & Applied behavior analysis & Clinical study data, & The average accuracy is 81\% \\  & & & collaborative filtering & to 84\% \\ \hline \multirow{4}{*}{[PERSON] et al., 2022 [11]} & Identifying autism in & \multirow{4}{*}{Machine-learning algorithms, feature extraction, naive Bayes} & \multirow{4}{*}{Accuracy is 87.50\%} \\  & & children using & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of the proposed work with previous works. Based on the overall study, several observations can be made. The overall contributions of the conducted study are as follows:  
* In both the datasets, i.e., toddlers and adults, it is observed that the maximum number of cases belonged to White European Ethnicity;
* In the case of toddlers, there are more male cases of ASD than females. In the case of adults, there are more female cases;
* Jaundice does not have any significant impact on ASD, as the results from both datasets are not consistent;
* Most ASD cases in toddlers are seen around three years, i.e., thirty-six months. Likewise, most ASD cases in adults fall between 20 to 30 years;
* This study proposed two deep neural network architectures, i.e., CNN-LSTM-PSO and GRU-CNN, for ASD detection in toddlers and adults (multiple datasets). While both models display satisfactory results, CNN-LSTM-PSO performs relatively better than other machine-learning algorithms;
* This study considers thirteen algorithms, including traditional ML algorithms, ensembles, and neural networks, performing extensive analysis on ASD;
* The study evaluated all the algorithms using multiple evaluation parameters, i.e., Accuracy, Precision, Recall, and F-1 Score, respectively;
* The optimization techniques considered for the study are Particle Swarm Optimization (combined with CNN-LSTM) and Adam (neural networks). The techniques are useful in minimizing the cost function, thereby improving model accuracy;
* The study deploys several AI fairness methods to eliminate bias. The bias elimination techniques proposed in this study are feature engineering techniques like handling outliers, transformations, and scaling. Apart from that, the SMOTE technique has been included to solve the class imbalance problem. Finally, we have deployed optimization methods for achieving efficient performance. The data obtained from two independent datasets has been thoroughly analyzed for bias elimination using feature engineering methods.