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A comparative study on autism among children using machine learning classification

Autism Spectrum Disorder (ASD) is a neurodevelopment that affects communication and behavior in humans. It is a condition associated with a complex brain disorder, leading to significant changes in a human being’s social interaction and behavior. Typically to detect toddlers who have ASD through scr...

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Main Authors: Ainie Hayati, Noruzman, Ngahzaifa, Ab Ghani, Nor Saradatul Akmar, Zulkifli
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/42193/1/A%20comparative%20study%20on%20autism%20among%20children.pdf
http://umpir.ump.edu.my/id/eprint/42193/2/A%20comparative%20study%20on%20autism%20among%20children%20using%20machine%20learning%20classification_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42193/
https://doi.org/10.1007/978-3-030-85990-9_12
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spelling my.ump.umpir.421932024-10-30T04:21:57Z http://umpir.ump.edu.my/id/eprint/42193/ A comparative study on autism among children using machine learning classification Ainie Hayati, Noruzman Ngahzaifa, Ab Ghani Nor Saradatul Akmar, Zulkifli QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Autism Spectrum Disorder (ASD) is a neurodevelopment that affects communication and behavior in humans. It is a condition associated with a complex brain disorder, leading to significant changes in a human being’s social interaction and behavior. Typically to detect toddlers who have ASD through screening tests is very expensive and time-consuming. Typically, detecting toddlers who have ASD through screening tests is very expensive and time-consuming. However, with machine learning technology today, autism can be diagnosed efficiency and accuracy. This study aims to analyze and make a comparison on which prediction model that gives a high accuracy after the feature selection. The importance of attributes is investigated using correlation and the predictive models are constructed for the detection of this disorder in children. The dataset consists of 1054 instances and each instance includes 19 attributes. Experimental results clearly show that using feature selection with 10 attributes can lead the impact of accuracy with predictive model of Random Forest (RF) returns the highest accuracy with 94.78%. The findings also indicated that the number of questions in screening tools can be reduced and give an impact with the good results. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42193/1/A%20comparative%20study%20on%20autism%20among%20children.pdf pdf en http://umpir.ump.edu.my/id/eprint/42193/2/A%20comparative%20study%20on%20autism%20among%20children%20using%20machine%20learning%20classification_ABS.pdf Ainie Hayati, Noruzman and Ngahzaifa, Ab Ghani and Nor Saradatul Akmar, Zulkifli (2022) A comparative study on autism among children using machine learning classification. In: Lecture Notes in Networks and Systems. International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2021 , 25 - 26 June 2021 , Al Buraimi. pp. 131-140., 322. ISSN 2367-3370 (Published) https://doi.org/10.1007/978-3-030-85990-9_12
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Ainie Hayati, Noruzman
Ngahzaifa, Ab Ghani
Nor Saradatul Akmar, Zulkifli
A comparative study on autism among children using machine learning classification
description Autism Spectrum Disorder (ASD) is a neurodevelopment that affects communication and behavior in humans. It is a condition associated with a complex brain disorder, leading to significant changes in a human being’s social interaction and behavior. Typically to detect toddlers who have ASD through screening tests is very expensive and time-consuming. Typically, detecting toddlers who have ASD through screening tests is very expensive and time-consuming. However, with machine learning technology today, autism can be diagnosed efficiency and accuracy. This study aims to analyze and make a comparison on which prediction model that gives a high accuracy after the feature selection. The importance of attributes is investigated using correlation and the predictive models are constructed for the detection of this disorder in children. The dataset consists of 1054 instances and each instance includes 19 attributes. Experimental results clearly show that using feature selection with 10 attributes can lead the impact of accuracy with predictive model of Random Forest (RF) returns the highest accuracy with 94.78%. The findings also indicated that the number of questions in screening tools can be reduced and give an impact with the good results.
format Conference or Workshop Item
author Ainie Hayati, Noruzman
Ngahzaifa, Ab Ghani
Nor Saradatul Akmar, Zulkifli
author_facet Ainie Hayati, Noruzman
Ngahzaifa, Ab Ghani
Nor Saradatul Akmar, Zulkifli
author_sort Ainie Hayati, Noruzman
title A comparative study on autism among children using machine learning classification
title_short A comparative study on autism among children using machine learning classification
title_full A comparative study on autism among children using machine learning classification
title_fullStr A comparative study on autism among children using machine learning classification
title_full_unstemmed A comparative study on autism among children using machine learning classification
title_sort comparative study on autism among children using machine learning classification
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/42193/1/A%20comparative%20study%20on%20autism%20among%20children.pdf
http://umpir.ump.edu.my/id/eprint/42193/2/A%20comparative%20study%20on%20autism%20among%20children%20using%20machine%20learning%20classification_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42193/
https://doi.org/10.1007/978-3-030-85990-9_12
_version_ 1822924718445953024
score 13.235318

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