Diagnostic And Classification System For Kids With Learning Disabilities
Most experts are using manual techniques to diagnose dyslexia. Machine learning algorithms are capable enough to learn the knowledge of experts and thus, automation of the diagnosis process is possible. In this research, we propose an automated diagnostic and classification system. The system is tra...
| Main Authors: | , , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/19197/ http://ir.unimas.my/id/eprint/19197/3/DIAGNOSTIC.pdf |
| _version_ | 1848838658352742400 |
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| author | Rehman, Ullah Khan Lee, Julia Ai Cheng Yin, Bee Oon |
| author_facet | Rehman, Ullah Khan Lee, Julia Ai Cheng Yin, Bee Oon |
| author_sort | Rehman, Ullah Khan |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Most experts are using manual techniques to diagnose dyslexia. Machine learning algorithms are capable enough to learn the knowledge of experts and thus, automation of the diagnosis process is possible. In this research, we propose an automated diagnostic and classification system. The system is trained by pre-classified data of 857 school children scores in spelling and reading. The twenty-fifth percentile was applied on the scores to label the data. The scores of the twenty-fifth percentile and below were marked as indicators of children who were likely to have dyslexia while the scores above the twenty-fifth percentile were considered to be indicators of children who were non-dyslexic. The system has three components: the diagnostic module is a pre-screening application that can be used by experts, trained users and parents for detecting the symptoms of dyslexia. The second module is classification, which classifies the kids into two groups, non-dyslexics and suspicious for dyslexia in spelling and reading. A third module is an analysis tool for researchers. The results show that 23% of children were at risk for dyslexia in the training data and 20.7% in the testing data with 98% of
accuracy. |
| first_indexed | 2025-11-15T06:59:03Z |
| format | Proceeding |
| id | unimas-19197 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:59:03Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-191972022-08-22T03:56:48Z http://ir.unimas.my/id/eprint/19197/ Diagnostic And Classification System For Kids With Learning Disabilities Rehman, Ullah Khan Lee, Julia Ai Cheng Yin, Bee Oon HM Sociology Most experts are using manual techniques to diagnose dyslexia. Machine learning algorithms are capable enough to learn the knowledge of experts and thus, automation of the diagnosis process is possible. In this research, we propose an automated diagnostic and classification system. The system is trained by pre-classified data of 857 school children scores in spelling and reading. The twenty-fifth percentile was applied on the scores to label the data. The scores of the twenty-fifth percentile and below were marked as indicators of children who were likely to have dyslexia while the scores above the twenty-fifth percentile were considered to be indicators of children who were non-dyslexic. The system has three components: the diagnostic module is a pre-screening application that can be used by experts, trained users and parents for detecting the symptoms of dyslexia. The second module is classification, which classifies the kids into two groups, non-dyslexics and suspicious for dyslexia in spelling and reading. A third module is an analysis tool for researchers. The results show that 23% of children were at risk for dyslexia in the training data and 20.7% in the testing data with 98% of accuracy. 2017 Proceeding NonPeerReviewed text en http://ir.unimas.my/id/eprint/19197/3/DIAGNOSTIC.pdf Rehman, Ullah Khan and Lee, Julia Ai Cheng and Yin, Bee Oon (2017) Diagnostic And Classification System For Kids With Learning Disabilities. In: UNIMAS Silver Jubilee Conference 2017, October 2017, Pullman Hotel, Kuching. (Unpublished) |
| spellingShingle | HM Sociology Rehman, Ullah Khan Lee, Julia Ai Cheng Yin, Bee Oon Diagnostic And Classification System For Kids With Learning Disabilities |
| title | Diagnostic And Classification System For Kids With Learning Disabilities |
| title_full | Diagnostic And Classification System For Kids With Learning Disabilities |
| title_fullStr | Diagnostic And Classification System For Kids With Learning Disabilities |
| title_full_unstemmed | Diagnostic And Classification System For Kids With Learning Disabilities |
| title_short | Diagnostic And Classification System For Kids With Learning Disabilities |
| title_sort | diagnostic and classification system for kids with learning disabilities |
| topic | HM Sociology |
| url | http://ir.unimas.my/id/eprint/19197/ http://ir.unimas.my/id/eprint/19197/3/DIAGNOSTIC.pdf |