# Utilizing Constructed Neural Networks for Autism Screening
## 4 Results
### Application Details and Experimental Results  
The classifiers' application details and parameterization are explained in this part, followed by experimental results.  
Table 3 provides critical parameters of the INN and NNC methods using the gameplay dataset. This table outlines the specific settings used in these methods, which are crucial for understanding the experimental setup and the nature of the ML algorithms employed. Five hundred chromosomes were utilized in the models, suggesting a broad search space for finding the optimal network configuration. The number of 200 iterations allows for an extensive exploration of the solution space. The 10% selection rate implies that only the top 10% of chromosomes will be used to create the next generation based on their fitness or performance. This rate is crucial for balancing the exploration and exploitation in the algorithm. The 5% mutation rate used signifies a moderate level of randomness in the evolution of the solutions.  
Next, Table 4 reports on the experimental results. It displays the performance comparison of the classification methods. The RBF neural network method shows an error rate of 18.70%, which is relatively high compared to others. Its precision is 0.54, and recall is 0.58, indicating moderate effectiveness in classifying the dataset accurately. The MLP BFGS method shows a similar classifying attitude to the RBF, presenting an error rate of 19.28%, the highest compared to others. Its precision is 0.58, and recall is 0.60, indicating moderate effectiveness in accurately classifying the dataset. Next, the MLP LBFGS method has an error rate of 17.31%, slightly better than RBF and MLP BFGS. The precision of 0.56 is somewhat lower but has a higher recall of 0.68, suggesting it is better at identifying relevant instances. The NNC model has a further reduced error rate of 16.05%, with precision equal to MLP BFGS (0.58) but better recall (0.66). This indicates that it is more effective than MLP BFGS and MLP LBFGS in accurately classifying the dataset. Finally, the INN method demonstrates the best performance with the lowest error rate of 11.77%, a high precision of 0.75, and a recall of 0.66. The results suggest that INN is better at avoiding false classifications (high precision) and maintains a reasonable rate of correctly identifying relevant instances (recall).  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Variable** & **Mann-Whitney U** & **Wilcoxon W** & **Z** & \(p\)**-Value** \\ \hline Recognition & 5019 & 6009 & \(-\)1.969 & 0.05 \\ Perception/Discrimination & 5329 & 43,555 & \(-\)1.329 & 0.18 \\ Sustained Attention & 5213.5 & 6203.5 & \(-\)1.892 & 0.06 \\ Cognitive Flexibility & 5980.5 & 44,206.5 & \(-\)0.162 & 0.87 \\ Empathy & 4539.5 & 5529.5 & \(-\)2.689 & 0.01 \\ Conditioned Play & 5266 & 6256 & \(-\)1.419 & 0.16 \\ Audiometry & & & & \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Number of Chromosomes & 500 \\ Number of Generations & 200 \\ Selection Rate & 10\% \\ Mutation Rate & 5\% \\ \hline \end{tabular}
\end{table}
Table 3: INN and NNC values.