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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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Science and Business Media Deutschland GmbH
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39641/ http://umpir.ump.edu.my/id/eprint/39641/1/A%20Comparative%20Study%20on%20Autism%20Among%20Children%20Using%20Machine.pdf http://umpir.ump.edu.my/id/eprint/39641/2/A%20comparative%20study%20on%20autism%20among%20children%20using%20machine%20learning%20classi%EF%AC%81cation_ABS.pdf |
| _version_ | 1848825823486803968 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:35:03Z |
| format | Conference or Workshop Item |
| id | ump-39641 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:35:03Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-396412023-12-13T07:37:12Z http://umpir.ump.edu.my/id/eprint/39641/ 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/39641/1/A%20Comparative%20Study%20on%20Autism%20Among%20Children%20Using%20Machine.pdf pdf en http://umpir.ump.edu.my/id/eprint/39641/2/A%20comparative%20study%20on%20autism%20among%20children%20using%20machine%20learning%20classi%EF%AC%81cation_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 (263669). ISSN 2367-3370 ISBN 978-303085989-3 (Published) https://doi.org/10.1007/978-3-030-85990-9_12 |
| 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 |
| title | 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_short | A comparative study on autism among children using machine learning classification |
| title_sort | comparative study on autism among children using machine learning classification |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/39641/ http://umpir.ump.edu.my/id/eprint/39641/ http://umpir.ump.edu.my/id/eprint/39641/1/A%20Comparative%20Study%20on%20Autism%20Among%20Children%20Using%20Machine.pdf http://umpir.ump.edu.my/id/eprint/39641/2/A%20comparative%20study%20on%20autism%20among%20children%20using%20machine%20learning%20classi%EF%AC%81cation_ABS.pdf |