A Systematic Review on Prognosis of Autism Using Machine Learning Techniques

Title

A Systematic Review on Prognosis of Autism Using Machine Learning Techniques

Creator

Malviya M.; Chandra J.

Description

Quality of life (QoL) and QoL predictors have become crucial in the pandemic. Neurological anomalies are at the highest level of QoL threats. Autism is a multisystem disorder that causes behavioural, neurological, cognitive, and physical differences. Recent studies state that neurological disorders can result in dysfunction of the brain or whole nervous system which may cause other symptoms of Autism. The paper focuses on reviewing various Machine Learning techniques used for diagnosing Autism at an early age with the help of multiple datasets. The study of brain Magnetic Resonance Imaging (MRI) provides astute knowledge of brain structure that helps to study any minor to significant changes inside the brain that have emerged due to the disorder. Early diagnosis leads to a healthy life by getting timely treatment and training. "Early diagnosis of autism spectrum disorder" is an objective and one of the prime goals of health establishments worldwide. The research paper aims to systematically review and find which machine learning algorithms are efficient for the prognosis of autism. The Electrochemical Society

Source

ECS Transactions, Vol-107, No. 1, pp. 11623-11632.

Date

2022-01-01

Publisher

Institute of Physics

Subject

Autism; deep learning; Machine Learning; MRI; prognosis

Coverage

Malviya M., Department of Computer Science, Christ Deemed to be University, Bangalore, India; Chandra J., Department of Computer Science, Christ Deemed to be University, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 19386737; ISBN: 978-160768539-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1149/10701.11623ecst

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130573543&doi=10.1149%2f10701.11623ecst&partnerID=40&md5=ce3fa7c3fd1ac5411bbd831a3e052d08