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Enhancing autism screening classification using feature selection and stacking classifier

The current screening process for early detection of autism spectrum disorders (ASD) is time-consuming and costly., requiring numerous questions about various aspects of a child's development. To address this issue., this study integrates the Recursive Feature Elimination (RFE) method into a st...

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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: Institute of Electrical and Electronics Engineers Inc. 2023
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/40335/1/Enhancing%20autism%20screening%20classification%20using%20feature.pdf
http://umpir.ump.edu.my/id/eprint/40335/2/Enhancing%20autism%20screening%20classification%20using%20feature%20selection%20and%20stacking%20classifier_ABS.pdf
http://umpir.ump.edu.my/id/eprint/40335/
https://doi.org/10.1109/ICSECS58457.2023.10256309
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spelling my.ump.umpir.403352024-04-16T04:11:18Z http://umpir.ump.edu.my/id/eprint/40335/ Enhancing autism screening classification using feature selection and stacking classifier 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) The current screening process for early detection of autism spectrum disorders (ASD) is time-consuming and costly., requiring numerous questions about various aspects of a child's development. To address this issue., this study integrates the Recursive Feature Elimination (RFE) method into a stacking ensemble classifier., allowing to identify the most important and effective features from the autism screening tool. This approach is aimed to create a simplified version of the autism screening and to make the screening process faster and more efficient by reducing the number of questions in autism screening tool. The proposed model provides a more efficient and simplified alternative for autism screening., allowing for early decision-making based on consistent and precise results. With 0.9760% accuracy results in predicting ASD traits., it is hoped that these findings will be an alternative option to make the screening questions much simpler while also providing an alternative to parents in predicting autism earlier and with less time. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40335/1/Enhancing%20autism%20screening%20classification%20using%20feature.pdf pdf en http://umpir.ump.edu.my/id/eprint/40335/2/Enhancing%20autism%20screening%20classification%20using%20feature%20selection%20and%20stacking%20classifier_ABS.pdf Ainie Hayati, Noruzman and Ngahzaifa, Ab Ghani and Nor Saradatul Akmar, Zulkifli (2023) Enhancing autism screening classification using feature selection and stacking classifier. In: 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023 , 25-27 August 2023 , Penang. pp. 408-413. (192961). ISBN 979-835031093-1 https://doi.org/10.1109/ICSECS58457.2023.10256309
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
Enhancing autism screening classification using feature selection and stacking classifier
description The current screening process for early detection of autism spectrum disorders (ASD) is time-consuming and costly., requiring numerous questions about various aspects of a child's development. To address this issue., this study integrates the Recursive Feature Elimination (RFE) method into a stacking ensemble classifier., allowing to identify the most important and effective features from the autism screening tool. This approach is aimed to create a simplified version of the autism screening and to make the screening process faster and more efficient by reducing the number of questions in autism screening tool. The proposed model provides a more efficient and simplified alternative for autism screening., allowing for early decision-making based on consistent and precise results. With 0.9760% accuracy results in predicting ASD traits., it is hoped that these findings will be an alternative option to make the screening questions much simpler while also providing an alternative to parents in predicting autism earlier and with less time.
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 Enhancing autism screening classification using feature selection and stacking classifier
title_short Enhancing autism screening classification using feature selection and stacking classifier
title_full Enhancing autism screening classification using feature selection and stacking classifier
title_fullStr Enhancing autism screening classification using feature selection and stacking classifier
title_full_unstemmed Enhancing autism screening classification using feature selection and stacking classifier
title_sort enhancing autism screening classification using feature selection and stacking classifier
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40335/1/Enhancing%20autism%20screening%20classification%20using%20feature.pdf
http://umpir.ump.edu.my/id/eprint/40335/2/Enhancing%20autism%20screening%20classification%20using%20feature%20selection%20and%20stacking%20classifier_ABS.pdf
http://umpir.ump.edu.my/id/eprint/40335/
https://doi.org/10.1109/ICSECS58457.2023.10256309
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score 13.234276

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