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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Semarak Ilmu Publishing
2026
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42939/ http://umpir.ump.edu.my/id/eprint/42939/1/Embedded%20Feature%20Importance%20with%20Threshold-based%20Selection%20for%20Optimal%20Feature%20Subset%20in%20Autism%20Screening.pdf |
| _version_ | 1848826739770261504 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:49:37Z |
| format | Article |
| id | ump-42939 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:49:37Z |
| publishDate | 2026 |
| publisher | Semarak Ilmu Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/42939/ http://umpir.ump.edu.my/id/eprint/42939/ http://umpir.ump.edu.my/id/eprint/42939/1/Embedded%20Feature%20Importance%20with%20Threshold-based%20Selection%20for%20Optimal%20Feature%20Subset%20in%20Autism%20Screening.pdf |