# Utilizing Constructed Neural Networks for Autism Screening
###### Abstract
Diagnostic examinations are used to further examine and identify developmental disorders in children who are at risk through surveillance and screening procedures and are typically conducted by developmental experts [11; 12]. The intricate and diverse nature of symptoms and manifestations in ASDs present significant challenges in their identification, often making the tasks of monitoring, screening, and diagnosis complicated due to imprecise characterizations and the presence of comorbid conditions [12; 14; 15; 16; 17; 18; 19]. This complexity is apparent in the fact that more than one in three children with ASD display symptoms that also meet criteria for other disorders, resulting in a variety of possible diagnostic combinations [20]. Furthermore, traditional evaluation methods often rely on subjective assessments and prolonged evaluation processes, leading to delays and inaccuracies in diagnosis [21]. Innovative diagnostic processes help customize ASD treatments, improving brain flexibility and mitigating symptoms. This leads to a better quality of life for children and their families [22]. Clinicians must build policies and practices by considering the type of condition and the child's abilities and skills for individualized interventions [23; 24; 25]. The field of machine learning (ML) has recently gained much attention for its ability to distinguish children with typical development (TD) from those with ASD [22]. Automated measuring tools are increasingly used in ASD research for decision-making, categorization, and clinical assessment, offering new prospects for aiding clinical decision-making [14; 22; 25; 26]. The identification and diagnosis of ASD involves various methods like brain imaging (PET, SPECT, fNIRS, EEG, fMRI, etc.) [27; 28; 29; 30], diffusion tensor imaging [31], and biometrics to analyze neurological and behavioral aspects [32]. Also, gesture analysis using motion capture, sensory input assessment, and eye tracking are employed [33; 25; 34; 35]. These techniques, though comprehensive, face challenges like data accessibility and sensory sensitivities in ASD children. ML is increasingly used to process these data for a more effective diagnosis and treatment of ASD [36]. For instance, ML algorithms, in combination with eye-tracking technology, are increasingly showing significance in the early identification and diagnosis of ASD utilizing various stimuli, tasks, datasets, and algorithms [33; 37; 25; 32; 38; 39]. Evidence of ML methods combining physiological data (EEG) with behavioral data (eye fixation and facial expression) has been developed to identify children with ASD [40]. This hybrid fusion strategy reported EEG to be the most effective in distinguishing between children with ASD and TD children. It achieved an accuracy rate of 87.50%, reducing costs while increasing efficiency. Furthermore, child's play is a natural process that helps children learn essential life skills. Serious Games (SGs) use play to enhance purpose in education, healthcare, marketing, science, and more [41]. SGs have been employed for mental health issues, physical activity rehabilitation, and other areas. These games are not only for entertainment but can also serve as learning tools and cultivate skills. SGs can be designed with explicit instructional objectives, which immerse children in activities that simulate real-life situations and difficulties--all within a supervised and secure setting [41]. Moreover, it is documented that children with NDs can enjoy better, anxiety-free activities with SGs [42; 43]. By integrating ML into SGs, they may become powerful tools for the automated screening and assessment of ASD [32; 44]. Studies show that ML in SGs can aid the classification of ASD and help children with early identification, diagnostic assessment, objective metrics, engagement, motivation, and tailored therapy [35; 45]. Virtual Reality (VR) adventing SGs can enhance social skills in ASD adolescents with multisensory interactions [46].