# Utilizing Constructed Neural Networks for Autism Screening
## 5 Conclusions  
This study's goal was to investigate the use of constructed neural network models for the early detection of ASD in a gameplay dataset, which is a practical and child-friendly tool. The study aimed to use gameplay data as an engaging and non-intrusive medium for assessment; differentiate between children diagnosed with ASD and TD children; and provide a supportive tool for clinicians in diagnosing ASD, enhancing the screening process with the help of ML. The study recorded significant statistical differences between TD and ASD children using the Mann-Whitney U test for verbal and intellectual ability, articulation, phonology, syntax, pragmatic perception, sequencing, memory, recognition, and empathy. The best-performing ML model was INN, demonstrating the lowest error rate, highest precision, and good recall. It proved to be the most effective model in distinguishing children with ASD from typically developed ones in the gameplay dataset. Combining gameplay data and ML, we propose a novel, child-friendly approach to ASD screening, providing a non-intrusive and engaging way to assess children. This method has the potential to support clinicians in early diagnosis, which is crucial for timely intervention in ASD. However, the study involved a smaller proportion of children with ASD (44 out of 320), indicating that sample size and diversity could be considered limitations of this study. A more extensive and diverse sample could provide more robust and generalizable results. While the INN model showed promising results, its effectiveness in different contexts or with other data types remains to be tested. Future research may address broader and more diverse data collection involving a more extensive and diverse participant pool, including variations in age, cultural backgrounds, and ASD severity levels. Additionally, expansion to different data types could provide a more holistic understanding of ASD. Moreover, supplementary development and the refinement of algorithms and comparisons with other emerging ML methods could enhance their effectiveness and accuracy. In summary, this study presents innovative methods for ASD detection utilizing machine learning and gameplay data. Future research should focus on addressing limitations and exploring potential applications. Conceptualization, E.I.T. and I.G.T. ; methodology, E.I.T. and G.T. ; software, I.G.T. ; validation, E.I.T. and J.P.; formal analysis, E.I.T., G.T. and I.G.T. ; investigation, G.T. and K.P. ; resources, E.I.T. and I.G.T. ; data curation, G.T. and J.P.; writing--original draft preparation, E.I.T., G.T. and I.G.T. ; writing--review and editing, E.I.T., J.P. and I.G.T. ; visualization, E.I.T. ; supervision, E.I.T. ; project administration, E.I.T. ; funding acquisition, E.I.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the project titled \"Smart Computing Models, Sensors, and Early Diagnostic Speech and Language Deficiencies Indicators in Child Communication\" (MIS: 5033088), supported by the European Regional Development Fund (ERDF). The study was conducted following the Declaration of Helsinki and approved by the Research Ethics Committee of the University of Ioannina, Greece (protocol code 18435/15 May 2020). Informed Consent Statement: Informed written consent was obtained from all participating parents after they were informed about the study's compliance with GDPR. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Error Rate (\%)** & **Precision** & **Recall** \\ \hline RBF & 18.70\% & 0.54 & 0.58 \\ MLP BFGS & 19.28\% & 0.58 & 0.60 \\ MLP LBFGS & 17.31\% & 0.56 & 0.68 \\ NNC & 16.05\% & 0.58 & 0.66 \\ INN & 11.77\% & 0.75 & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparing Classification Methods with Error Rate (%), Precision, and Recall Metrics. Data Availability Statement: Data are contained within the article. We wish to thank all the participants for their valuable contributions to this study and the administrative and technical support.