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Embedded feature importance with threshold-based selection for optimal feature subset in autism screening

The early detection of autism spectrum disorders (ASD) in children poses significant challenges due to the dynamic and progressive nature of the symptoms. To The current screening process involves a lengthy and costly series of questions covering various aspects of a child's development. To add...

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Main Authors: Ainie Hayati, Noruzman, Ngahzaifa, Ab Ghani, Nor Saradatul Akmar, Zulkifli, Alhroob, Essam
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2026
Subjects:
QA75 Electronic computers. Computer science
T Technology (General)
Online Access:http://umpir.ump.edu.my/id/eprint/42939/1/Embedded%20Feature%20Importance%20with%20Threshold-based%20Selection%20for%20Optimal%20Feature%20Subset%20in%20Autism%20Screening.pdf
http://umpir.ump.edu.my/id/eprint/42939/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/5362
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spelling my.ump.umpir.429392024-11-19T00:28:53Z http://umpir.ump.edu.my/id/eprint/42939/ Embedded feature importance with threshold-based selection for optimal feature subset in autism screening Ainie Hayati, Noruzman Ngahzaifa, Ab Ghani Nor Saradatul Akmar, Zulkifli Alhroob, Essam QA75 Electronic computers. Computer science T Technology (General) The early detection of autism spectrum disorders (ASD) in children poses significant challenges due to the dynamic and progressive nature of the symptoms. To The current screening process involves a lengthy and costly series of questions covering various aspects of a child's development. To address this issue, we adopt the embedded feature selection method based on random forest and threshold-based to produce a simplified version questionnaire for Autism screening. The aim of this paper is to identify the most crucial and effective features from the Quantitative Checklist for Autism in Toddlers (Q-CHAT) by combining the strengths of threshold filtering and embedded random forest feature importance. This integration allows us to significantly reduce the number of screening questions while maintaining reliable and accurate results. Our proposed method yields a streamlined alternative to traditional screening, extracting just eight key features that achieves an impressive 96% accuracy performance. This promising approach holds the potential to revolutionize early detection and intervention programs for toddlers with autism, ultimately leading to improved outcomes. Semarak Ilmu Publishing 2026 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/42939/1/Embedded%20Feature%20Importance%20with%20Threshold-based%20Selection%20for%20Optimal%20Feature%20Subset%20in%20Autism%20Screening.pdf Ainie Hayati, Noruzman and Ngahzaifa, Ab Ghani and Nor Saradatul Akmar, Zulkifli and Alhroob, Essam (2026) Embedded feature importance with threshold-based selection for optimal feature subset in autism screening. Journal of Advanced Research in Applied Sciences and Engineering Technology, 59 (1). pp. 12-22. ISSN 2462-1943. (Published) https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/5362
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
topic QA75 Electronic computers. Computer science
T Technology (General)
spellingShingle QA75 Electronic computers. Computer science
T Technology (General)
Ainie Hayati, Noruzman
Ngahzaifa, Ab Ghani
Nor Saradatul Akmar, Zulkifli
Alhroob, Essam
Embedded feature importance with threshold-based selection for optimal feature subset in autism screening
description The early detection of autism spectrum disorders (ASD) in children poses significant challenges due to the dynamic and progressive nature of the symptoms. To The current screening process involves a lengthy and costly series of questions covering various aspects of a child's development. To address this issue, we adopt the embedded feature selection method based on random forest and threshold-based to produce a simplified version questionnaire for Autism screening. The aim of this paper is to identify the most crucial and effective features from the Quantitative Checklist for Autism in Toddlers (Q-CHAT) by combining the strengths of threshold filtering and embedded random forest feature importance. This integration allows us to significantly reduce the number of screening questions while maintaining reliable and accurate results. Our proposed method yields a streamlined alternative to traditional screening, extracting just eight key features that achieves an impressive 96% accuracy performance. This promising approach holds the potential to revolutionize early detection and intervention programs for toddlers with autism, ultimately leading to improved outcomes.
format Article
author Ainie Hayati, Noruzman
Ngahzaifa, Ab Ghani
Nor Saradatul Akmar, Zulkifli
Alhroob, Essam
author_facet Ainie Hayati, Noruzman
Ngahzaifa, Ab Ghani
Nor Saradatul Akmar, Zulkifli
Alhroob, Essam
author_sort Ainie Hayati, Noruzman
title Embedded feature importance with threshold-based selection for optimal feature subset in autism screening
title_short Embedded feature importance with threshold-based selection for optimal feature subset in autism screening
title_full Embedded feature importance with threshold-based selection for optimal feature subset in autism screening
title_fullStr Embedded feature importance with threshold-based selection for optimal feature subset in autism screening
title_full_unstemmed Embedded feature importance with threshold-based selection for optimal feature subset in autism screening
title_sort embedded feature importance with threshold-based selection for optimal feature subset in autism screening
publisher Semarak Ilmu Publishing
publishDate 2026
url http://umpir.ump.edu.my/id/eprint/42939/1/Embedded%20Feature%20Importance%20with%20Threshold-based%20Selection%20for%20Optimal%20Feature%20Subset%20in%20Autism%20Screening.pdf
http://umpir.ump.edu.my/id/eprint/42939/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/5362
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score 13.235318

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