AUTISM SPECTRUM DISORDER DETECTION USING MACHINE LEARNING

Abstract

This article examines the visual preferences of autistic children in order to identify specific patterns, such as repetitive behavior, and focus on certain elements of the visual content, such as geometric shapes, etc. To analyze visual preferences, the research team collected the experimental data of two groups of children: those diagnosed with Autism Spectrum Disorders and typically developing children. Based on the received data, a model was trained to detect autism with the usage of machine learning. In addition, the machine was safely tested on children and showed the possibility of detecting Autism Spectrum Disorders in 40% of children with autism. The study was conducted on a web platform specially designed for the young audience, which allows them to track the direction of their gaze. The obtained results also indicate that children with autism give visual preference to geometric shapes with dynamic scene changes. The implementation of this system will be useful for early detection of Autism Spectrum Disorders due to the wide accessibility of this web platform and its beneficence as a reliable screening tool. The aim of the research is to create an innovative software that will provide an opportunity to identify Autism Spectrum Disorder using machine learning.