# Utilizing Constructed Neural Networks for Autism Screening
## 3 Methods  
This study builds upon the research project \"Smart Computing Models, Sensors, and Early Diagnostic Speech and Language Deficiencies Indicators in Child Communication\", or in short, SmartSpeech, which was funded by the Region of Epirus and backed by the European Regional Development Fund (ERDF). A recruitment sample was conducted in response to requests from the health and education sectors to assist TD children and children with ASD. Parents were given a thorough briefing about the project's details and scope, the protocols involved, and the permission granted (Reg. Num. : 18435/15.5.2020) by the Research Ethics Committee of the University of Ioannina, Greece, in compliance with General Data Protection Regulation GDPR. Afterward, parents filled out the consent form. Participants were asked to register in the database and answer questions about their child's developmental characteristics. With the guidance of a clinician, each child interacted with the game designed for this task. Ultimately, the variables examined in the research were obtained from the gameplay. The study gathered responses from children who participated in playing the Serious Game on a mobile device. The game involved different activities related to screening and assessing individuals for ASD [87]. During the gameplay, the children complete several activities related to their developmental skills. Therefore, the automated procedure of this phase gathers many measurements to form a child's developmental profile and provides a rich database specifically designed for screening TD children from children with ASD. The responses of the child-player are represented as variables and generally can be hand motions on the touch screen like clicks, drag, and drops, as well as speech from the child's verbal responses to questions and directions. For the latter, the system incorporates an automatic procedure that records speech via a microphone and translates it into text. For this purpose, the software Whisper v20230314 performs multilingual speech recognition [88]. The software is free from [[https://github.com/openai/whisper](https://github.com/openai/whisper)]([https://github.com/openai/whisper](https://github.com/openai/whisper)) (accessed on 2 April 2023). Then, a software service was created to compare the transcripts of the audio files with the predefined answers, generating a score representing the matching percentage between the spoken and correct words. The overall game activities correspond to the assessment categories reported in Figure 3 below. There are, in total, 18 variables that comprise our dataset. Some of them contain word recognition, such as in Articulation and Phonology. All variables are scaled and take values in the range \([0,100]\). The participants in this study are mainly children aged 3 to 12 years old. The children were enrolled with the help and support of their parents, along with their written consent. In total, 320 children were recruited, of which 276 were typically developed, and the rest 44 of them had ASD. This dataset fed the machine learning models selected, namely MLP, INN, and NNC. Each model was trained and tested using the 10-fold validation method. The objective was to find the most suitable method for classification; that is, to distinguish the ASD population from the typically developed population. The goal was to establish a screening tool to help clinicians safely and accurately in their diagnostic procedures. Figure 3: Areas of assessment and corresponding variables. Our dataset underwent statistical analysis. In the next section, we present the means and standard deviations of the game scores for TD and ASD children and compare the statistical differences between the two groups. Since the dataset variables do not have a normal distribution, we used the non-parametric Mann-Whitney U Test. For the classification experiments, we evaluated the performance employing commonly used metrics in machine learning [89; 90; 91].