Toward Machine Learning-Based Psychological Assessment of Autism Spectrum Disorders in School and Community

Abstract

The sensory processing system of the human body is capable of collecting, developing, and integrating information through sensory organs. Sensory impairment has been discovered in children with autism spectrum disorder (ASD). People with ASD are susceptible to hyper/hypo-sensitivity that might cause changes in information management, and affect cognitive impairment and social reactions to everyday events. This article proposed a questionnaire based on ASD symptoms found in previous studies with 82 questions. Following that, a dataset is created by conducting a survey using the questionnaire. Several machine learning models that can identify ASD and its types are also compared. Among the machine learning models, the artificial neural network achieved an accuracy of 89.8%. Implicit measurements and ecologically sound settings have shown excellent precision in predicting outcomes and the correct classification of populations into categories.