Detecting child autism using classification techniques

Md Delowar Hossain; Ashad Kabir

doi:10.3233/SHTI190477

Detecting child autism using classification techniques

Md Delowar Hossain
, Ashad Kabir

Charles Sturt University

Research output: Book chapter/Published conference paper › Conference paper › peer-review

5 Citations (Scopus)

96 Downloads (Pure)

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 language	English
Title of host publication	17th World Congress of Medical and Health Informatics
Subtitle of host publication	Health and wellbeing E-networks for all
Editors	Lucila Ohno-Machado, Brigitte Seroussi
Place of Publication	Netherlands
Publisher	IOS Press
Pages	1447-1448
Number of pages	2
Volume	264
ISBN (Electronic)	9781643680033
ISBN (Print)	9781643680026
DOIs	https://doi.org/10.3233/SHTI190477
Publication status	Published - 2019
Event	17th World Congress on Medical and Health Informatics: MEDINFO 2019 - Cité Internationale - Congress Centre, Lyon, France Duration: 25 Aug 2019 → 30 Aug 2019 https://web.archive.org/web/20190630045459/https://www.medinfo-lyon.org/en/about-us/medinfo2019/ (Conference website on Wayback Machine) https://ebooks.iospress.nl/volume/medinfo-2019-health-and-wellbeing-e-networks-for-all-proceedings-of-the-17th-world-congress-on-medical-and-health-informatics (Proceedings) https://web.archive.org/save/https://imia-medinfo.org/wp/wp-content/uploads/2020/06/PROGRAM-Medinfo-2019.pdf (Program)

Publication series

Name	Studies in Health Technology and Informatics
Publisher	IOS Press
Volume	264
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	17th World Congress on Medical and Health Informatics
Abbreviated title	Health and wellbeing e-networks for all
Country/Territory	France
City	Lyon
Period	25/08/19 → 30/08/19
Internet address	https://web.archive.org/web/20190630045459/https://www.medinfo-lyon.org/en/about-us/medinfo2019/ (Conference website on Wayback Machine) https://ebooks.iospress.nl/volume/medinfo-2019-health-and-wellbeing-e-networks-for-all-proceedings-of-the-17th-world-congress-on-medical-and-health-informatics (Proceedings) https://web.archive.org/save/https://imia-medinfo.org/wp/wp-content/uploads/2020/06/PROGRAM-Medinfo-2019.pdf (Program)

Access to Document

10.3233/SHTI190477

30097196 pub paperFinal published version, 1.29 MBLicence: CC BY-NC

Honours Dissertation on "Detecting Autism Spectrum Disorder using Machine Learning"
Kabir, A. (Principal Supervisor)
2019 → 2020
Activity: Supervision/Examination/Mentoring › Honours Supervision

Cite this

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title = "Detecting child autism using classification techniques",

abstract = "Autism spectrum disorder (ASD) is a brain development disorder that restricts a person{\textquoteright}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.",

keywords = "Autism spectrum disorder, Classification techniques, Sequential Minimal Optimization",

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year = "2019",

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language = "English",

isbn = "9781643680026",

volume = "264",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press",

pages = "1447--1448",

editor = "Lucila Ohno-Machado and Brigitte Seroussi",

booktitle = "17th World Congress of Medical and Health Informatics",

address = "Netherlands",

note = "17th World Congress on Medical and Health Informatics : MEDINFO 2019, Health and wellbeing e-networks for all ; Conference date: 25-08-2019 Through 30-08-2019",

url = "https://web.archive.org/web/20190630045459/https://www.medinfo-lyon.org/en/about-us/medinfo2019/, https://ebooks.iospress.nl/volume/medinfo-2019-health-and-wellbeing-e-networks-for-all-proceedings-of-the-17th-world-congress-on-medical-and-health-informatics, https://web.archive.org/save/https://imia-medinfo.org/wp/wp-content/uploads/2020/06/PROGRAM-Medinfo-2019.pdf",

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Hossain, MD & Kabir, A 2019, Detecting child autism using classification techniques. in L Ohno-Machado & B Seroussi (eds), 17th World Congress of Medical and Health Informatics: Health and wellbeing E-networks for all. vol. 264, Studies in Health Technology and Informatics, vol. 264, IOS Press, Netherlands, pp. 1447-1448, 17th World Congress on Medical and Health Informatics, Lyon, France, 25/08/19. https://doi.org/10.3233/SHTI190477

Detecting child autism using classification techniques. / Hossain, Md Delowar; Kabir, Ashad.
17th World Congress of Medical and Health Informatics: Health and wellbeing E-networks for all. ed. / Lucila Ohno-Machado; Brigitte Seroussi. Vol. 264 Netherlands: IOS Press, 2019. p. 1447-1448 (Studies in Health Technology and Informatics; Vol. 264).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - Detecting child autism using classification techniques

AU - Hossain, Md Delowar

AU - Kabir, Ashad

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Autism spectrum disorder

KW - Classification techniques

KW - Sequential Minimal Optimization

U2 - 10.3233/SHTI190477

DO - 10.3233/SHTI190477

M3 - Conference paper

C2 - 31438174

SN - 9781643680026

VL - 264

T3 - Studies in Health Technology and Informatics

SP - 1447

EP - 1448

BT - 17th World Congress of Medical and Health Informatics

A2 - Ohno-Machado, Lucila

A2 - Seroussi, Brigitte

PB - IOS Press

CY - Netherlands

T2 - 17th World Congress on Medical and Health Informatics

Y2 - 25 August 2019 through 30 August 2019

ER -

Detecting child autism using classification techniques

Abstract

Publication series

Conference

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Activities

Honours Dissertation on "Detecting Autism Spectrum Disorder using Machine Learning"

Cite this