Editorial: Improving autism spectrum disorder diagnosis using machine learning techniques

Elbattah, Mahmoud; Ali Sadek Ibrahim, Osman; Dequen, Gilles

doi:10.3389/fninf.2024.1529839

Editorial: Improving autism spectrum disorder diagnosis using machine learning techniques

Elbattah, Mahmoud; Ali Sadek Ibrahim, Osman; Dequen, Gilles

Authors

Mahmoud Elbattah

Osman Ali Sadek Ibrahim

Gilles Dequen

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social communication, repetitive behaviors, and restricted interests (American Psychiatric Association, 2013). Early and accurate diagnosis is critical for effective intervention, enabling individuals with ASD to achieve better developmental outcomes and an improved quality of life. However, traditional diagnostic methods, often reliant on subjective behavioral observations, remain time-intensive and inconsistently accessible. This underscores an urgent need for innovative, scalable, and objective diagnostic tools (Rasul et al., 2024; Jeyarani and Senthilkumar, 2023).

Journal Article Type	Article
Acceptance Date	Nov 20, 2024
Online Publication Date	Dec 6, 2024
Publication Date	Dec 6, 2024
Deposit Date	Dec 8, 2024
Publicly Available Date	Dec 11, 2024
Journal	Frontiers in Neuroinformatics
Electronic ISSN	1662-5196
Publisher	Frontiers Media
Peer Reviewed	Peer Reviewed
Volume	18
Article Number	1529839
DOI	https://doi.org/10.3389/fninf.2024.1529839
Public URL	https://uwe-repository.worktribe.com/output/13515083