Title: Autism spectrum disorder diagnosis and machine learning: a review

Authors: Chandan Jyoti Kumar; Priti Rekha Das; Anil Hazarika

Addresses: Department of Computer Science and IT, Cotton University, Guwahati, Assam, India ' Department of Psychology, Gauhati University, Guwahati, Assam, India ' Department of Physics, Cotton University, Guwahati, Assam, India

Abstract: Autism spectrum disorder (ASD) with global prevalence estimate of approximately 1%, makes it a major social health concern. To make the diagnostic process of ASD faster, convenient and more accurate the researchers have started to apply a dozen of machine learning techniques. This review considers major publications of last decade to identify various aspects of machine learning research in ASD diagnosis. Findings of diagnostic tools and techniques are highlighted so as to detect significant features for machine learning models. Based on types of data, the article categorises the diagnostic research in two broad categories: behavioural and neuro-imaging. In addition, it explores the various findings of these behavioural and neuro-imaging techniques in ASD subjects and makes a detailed analysis of performance of these techniques in combination with different machine learning models for ASD diagnosis. This article highlights key research fields of ASD and discusses potential research direction in the future.

Keywords: autism spectrum disorder; ASD; machine learning; neuro-imaging; ASD datasets.

DOI: 10.1504/IJMEI.2022.126522

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.6, pp.512 - 527

Received: 12 Aug 2020
Accepted: 26 Dec 2020
Published online: 28 Oct 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article