Review On Early Autism Spectrum Disorder Detection in Children Using Machine Learning  Techniques

Shweta Ramtekkar; Dr. Gurudev Sawarkar; Prof. Harshali Ragite

doi:10.63363/aijfr.2026.v07i03.5758

Review On Early Autism Spectrum Disorder Detection in Children Using Machine Learning Techniques

Author(s)	Shweta Ramtekkar, Dr. Gurudev Sawarkar, Prof. Harshali Ragite
Country	India
Abstract	Autism Spectrum Disorder (ASD), a neurodevelopmental disorder that manifests through a variety of symptoms, is acombination of various challenges that affect an individual socially, communicatively, and behaviorally, among others. Thetreatment of ASD is based on a hierarchy of needs, from the most urgent to the least, the latter being the least in number dueto the efficacy of the early intervention. In the present day, the methods employed for diagnosis depend on the observation ofthe clinical staff and assessments that have been standardized which are subjective, lengthy, and sometimes unavailable inpoor areas. At the same time, clinically, the researchers have turned their attention to machine learning (ML) and artificialintelligence (AI) techniques because they can deal with the analysis of such an intricate mixture of factors like behavior,biology, and development supporting the diagnosis of ASD at an early stage. This paper is an extensive review that collectsand analyzes the most recent studies that are based on the use of ML for detecting ASD through the use of behavioralquestionnaires, speech signals, eye-tracking data, neuroimaging, and multimodal datasets. Among the different classificationalgorithms discussed are the Logistic Regression, Random Forest, Support Vector Machines, ensemble learning, and deeplearning models. The review also covers issues associated with such as dataset imbalance, limited interpretability, and lack ofreal-world validation, along with the non-availability of screening tools. Moreover, the role of explainable AI in increasing the acceptance of clinical trust is outlined. The authors of this review showcase the existing trends, constraints, and futuredirections of research aimed at the creation of trustworthy, interpretable, and accessible ML-based ASD screening systems.
Keywords	Autism Spectrum Disorder, Machine Learning, Early Detection, Explainable Artificial Intelligence, Multimodal Data Analysis etc.
Published In	Volume 7, Issue 3, May-June 2026
Published On	2026-05-29
DOI	https://doi.org/10.63363/aijfr.2026.v07i03.5758

View / Download PDF File

E-ISSN 3048-7641

doi

CrossRef DOI is assigned to each research paper published in our journal.

AIJFR DOI prefix is
10.63363/aijfr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2 Cover Page Vol 7 Isu 1 Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About AIJFR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-79-39-59	editor@aijfr.com

Advanced International Journal for Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Review On Early Autism Spectrum Disorder Detection in Children Using Machine Learning Techniques

Share this