Detecting child autism using classification techniques

Md Delowar Hossain, Ashad Kabir

Research output: Book chapter/Published conference paperConference paper

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Abstract

Autism spectrum disorder (ASD) is a brain development disorder that restricts a person’s communication abilities and social interaction capabilities from natural growth. In this paper, we have applied various supervised classificationtechniques to detect the presence of child autism. Our findings show that the Sequential Minimal Optimization (SMO) classifier performs best to detect ASD cases with the highest accuracy and minimum execution time and error rate. We also identify the most dominant features in dectecting child autism.
Original languageEnglish
Title of host publication17th World Congress of Medical and Health Informatics
Subtitle of host publicationHealth and wellbeing E-networks for all
EditorsLucila Ohno-Machado, Brigitte Seroussi
Place of PublicationNetherlands
PublisherIOS Press
Pages1447-1448
Number of pages2
Volume264
ISBN (Electronic)9781643680033
ISBN (Print)9781643680026
Publication statusPublished - 2019
Event17th World Congress on Medical and Health Informatics: MEDINFO 2019 - Lyon, France
Duration: 25 Aug 201930 Aug 2019
http://medinfo-lyon.org/en/about-us/medinfo2019/

Publication series

NameStudies in Health Technology and Informatics
PublisherIOS Press
Volume264
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference17th World Congress on Medical and Health Informatics
CountryFrance
CityLyon
Period25/08/1930/08/19
Internet address

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