Comparison of different classification methods for autism spectrum diagnosis

Abstract

Studies have found autism spectrum disorder is a diffuse developmental disease of the central nervous system. The majority of autism cases result from a combination of genetic predisposition and environmental factors that influence early brain development, despite a few being caused by genes alone. Traditional diagnosis of autism spectrum disorder is usually through interviews and questionnaires, which takes plenty of time and might be misdiagnosed. The primary purpose of this study is to compare different classification methods for distinguishing autism spectrum disorder from typical development by machine learning and deep learning in recent years. The experiments are conducted to discuss their strengths and weaknesses, which, in turn, results are presented for further research.