An Application of Neural Networks to Predicting Mastery of Learning Outcomes in the Treatment of Autism Spectrum Disorder

Abstract

We apply artificial neural networks to the task of predicting the mastery of learning outcomes in response to behavioral therapy for children diagnosed with autism spectrum disorder. We report results for a sample size of 726 children, the largest sample size reported for a study of this nature to date. Our results show that neural networks substantially outperform the linear regression models reported in previous studies, and demonstrate the benefits of leveraging more sophisticated machine learning techniques in the autism research domain.