# Utilizing Constructed Neural Networks for Autism Screening
###### Abstract  
Autism Spectrum Disorder is known to cause difficulties in social interaction and communication, as well as repetitive patterns of behavior, interests, or hobbies. These challenges can significantly affect the individual's daily life. Therefore, it is crucial to identify and assess children with Autism Spectrum Disorder early to significantly benefit the long-term health of children. Unfortunately, many children are not diagnosed or are misdiagnosed, which means they miss out on the necessary interventions. Clinicians and other experts face various challenges during the diagnostic process. Digital tools can facilitate early diagnosis effectively. This study aimed to explore the use of machine learning techniques on a dataset collected from a serious game designed for children with autism to investigate how these techniques can assist in classification and make the clinical process more efficient. The responses were gathered from children who participated in interactive games deployed on mobile devices, and the data were analyzed using various types of neural networks, such as multilayer perceptrons and constructed neural networks. The performance metrics of these models, including error rate, precision, and recall, were reported, and the comparative experiments revealed that the constructed neural network using the integer rule-based neural networks approach was superior. Based on the evaluation metrics, this method showed the lowest error rate of 11.77%, a high accuracy of 0.75, and a good recall of 0.66. Thus, it can be an effective way to classify both typically developed children and children with Autism Spectrum Disorder. Additionally, it can be used for automatic screening procedures in an intelligent system. The results indicate that clinicians could use these techniques to enhance conventional screening methods and contribute to providing better care for individuals with autism. Autism Spectrum Disorder (ASD); screening; classification; machine learning; constructed neural networks +
Footnote †: journal: Medical  
rates [5; 6]. As a neurodevelopmental condition, it has been characterized by specific criteria defined in DSM-5 [1; 2; 3; 7]. Individuals with ASD experience persistent shortages in social communication and interaction skills across numerous settings, and they exhibit repetitive and restrictive behaviors that can impair their functioning. ASD impacts personal, social, academic, and professional life. The term \"spectrum\" in ASD is used to describe a range of symptoms that vary in severity from person to person. Some individuals may have mild symptoms, while others may have moderate or more severe symptoms. It is common for individuals with ASD to also have other NDs, which can require intensive care. This can significantly impact the individual with profound impairments, their family, and the community [2; 3; 8; 9]. On the other end of the spectrum, individuals with ASDs can also be intellectually talented, verbally fluent, and independent adults [10]. This represents the opposite end of the functional spectrum. The recommendation of the American Academy of Pediatrics is to monitor and screen for developmental delays in children to detect disabilities such as ASD early and allow for prompt treatments supporting the child's overall growth and development [11; 12; 13]. Developmental surveillance for children using clinical judgment during health supervision visits involves a comprehensive approach, which includes the documentation of developmental history, reference to parents' concerns, a thorough observation of the child's behavior to identify potential risks, maintaining records, and sharing the findings with professionals outside the child's primary medical care provider [12; 13]. Developmental screening uses proven techniques at predetermined ages or whenever monitoring indicates a particular cause for concern.