FURIA stacking ensemble for ASD classification
Autism Spectrum Disorder (ASD) is an illness that affects many children nowadays. It is a condition that causes parents to be concerned about detecting early autistic traits in their children because they are not visible until an expert diagnosis them using screening tools. However, screening tools...
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/36922/ http://umpir.ump.edu.my/id/eprint/36922/1/FURIA%20stacking%20ensemble%20for%20asd%20classification.pdf |
| _version_ | 1848825119979339776 |
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| author | Ainie Hayati, Noruzman Ngahzaifa, Ab Ghani Nor Saradatulakmar, Zulkifli |
| author_facet | Ainie Hayati, Noruzman Ngahzaifa, Ab Ghani Nor Saradatulakmar, Zulkifli |
| author_sort | Ainie Hayati, Noruzman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Autism Spectrum Disorder (ASD) is an illness that affects many children nowadays. It is a condition that causes parents to be concerned about detecting early autistic traits in their children because they are not visible until an expert diagnosis them using screening tools. However, screening tools consist of specific criteria domain rules such as behaviour, communication, and social emotions that comprise various questions, resulting in excessive questions and significantly lengthening the autism screening process. Instead of relying on conventional domain expert rules, one possible solution is adapting fuzzy rules by proposing the Fuzzy Unordered Rule Induction Algorithm (FURIA) and the machine learning algorithms by collaborating them into the stacking ensemble framework. The results show that the stacking ensemble of FURIA with the Logistic Regression generated ten rules and a 95.072% classification accuracy with 0.965 precision in predicting ASD traits. These findings will be an alternative option to make the screening questions much simpler yet give an alternative to the parents in predicting earlier with less time and good accuracy results. |
| first_indexed | 2025-11-15T03:23:52Z |
| format | Conference or Workshop Item |
| id | ump-36922 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:23:52Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-369222023-02-07T04:29:43Z http://umpir.ump.edu.my/id/eprint/36922/ FURIA stacking ensemble for ASD classification Ainie Hayati, Noruzman Ngahzaifa, Ab Ghani Nor Saradatulakmar, Zulkifli QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Autism Spectrum Disorder (ASD) is an illness that affects many children nowadays. It is a condition that causes parents to be concerned about detecting early autistic traits in their children because they are not visible until an expert diagnosis them using screening tools. However, screening tools consist of specific criteria domain rules such as behaviour, communication, and social emotions that comprise various questions, resulting in excessive questions and significantly lengthening the autism screening process. Instead of relying on conventional domain expert rules, one possible solution is adapting fuzzy rules by proposing the Fuzzy Unordered Rule Induction Algorithm (FURIA) and the machine learning algorithms by collaborating them into the stacking ensemble framework. The results show that the stacking ensemble of FURIA with the Logistic Regression generated ten rules and a 95.072% classification accuracy with 0.965 precision in predicting ASD traits. These findings will be an alternative option to make the screening questions much simpler yet give an alternative to the parents in predicting earlier with less time and good accuracy results. 2022-11-15 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/36922/1/FURIA%20stacking%20ensemble%20for%20asd%20classification.pdf Ainie Hayati, Noruzman and Ngahzaifa, Ab Ghani and Nor Saradatulakmar, Zulkifli (2022) FURIA stacking ensemble for ASD classification. In: The 6th National Conference for Postgraduate Research (NCON-PGR 2022) , 15 November 2022 , Virtual Conference, Universiti Malaysia Pahang, Malaysia. p. 110.. (Published) https://ncon-pgr.ump.edu.my/index.php/en/?option=com_fileman&view=file&routed=1&name=E-BOOK%20NCON%202022%20.pdf&folder=E-BOOK%20NCON%202022&container=fileman-files |
| 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 Saradatulakmar, Zulkifli FURIA stacking ensemble for ASD classification |
| title | FURIA stacking ensemble for ASD classification |
| title_full | FURIA stacking ensemble for ASD classification |
| title_fullStr | FURIA stacking ensemble for ASD classification |
| title_full_unstemmed | FURIA stacking ensemble for ASD classification |
| title_short | FURIA stacking ensemble for ASD classification |
| title_sort | furia stacking ensemble for asd 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/36922/ http://umpir.ump.edu.my/id/eprint/36922/ http://umpir.ump.edu.my/id/eprint/36922/1/FURIA%20stacking%20ensemble%20for%20asd%20classification.pdf |