Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution
Developmental domains refer to different areas of a child’s growth and maturation, including physical, language, cognitive, and social–emotional skills. Understanding these domains helps parents, caregivers, and professionals track a child’s progress and identify potential areas of concern. Neverthe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/1/305 |
| _version_ | 1797359060486455296 |
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| author | Eugenia I. Toki Giorgos Tatsis Jenny Pange Ioannis G. Tsoulos |
| author_facet | Eugenia I. Toki Giorgos Tatsis Jenny Pange Ioannis G. Tsoulos |
| author_sort | Eugenia I. Toki |
| collection | DOAJ |
| description | Developmental domains refer to different areas of a child’s growth and maturation, including physical, language, cognitive, and social–emotional skills. Understanding these domains helps parents, caregivers, and professionals track a child’s progress and identify potential areas of concern. Nevertheless, due to the high level of heterogeneity and overlap, neurodevelopmental disorders may go undiagnosed in children for a crucial period. Detecting neurodevelopmental disorders at an early stage is fundamental. Digital tools like artificial intelligence can help clinicians with the early detection process. To achieve this, a new method has been proposed that creates artificial features from the original ones derived from the SmartSpeech project, using a feature construction procedure guided by the Grammatical Evolution technique. The new features from a machine learning model are used to predict neurodevelopmental disorders. Comparative experiments demonstrated that using the feature creation method outperformed other machine learning methods for predicting neurodevelopmental disorders. In many cases, the reduction in the test error reaches up to 65% to the next better one. |
| first_indexed | 2024-03-08T15:11:30Z |
| format | Article |
| id | doaj.art-3946879eb1d0472d807063071da139bb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T15:11:30Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3946879eb1d0472d807063071da139bb2024-01-10T14:51:41ZengMDPI AGApplied Sciences2076-34172023-12-0114130510.3390/app14010305Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical EvolutionEugenia I. Toki0Giorgos Tatsis1Jenny Pange2Ioannis G. Tsoulos3Department of Speech and Language Therapy, School of Health Sciences, University of Ioannina, Panepistimioupoli B’, 45500 Ioannina, GreeceDepartment of Speech and Language Therapy, School of Health Sciences, University of Ioannina, Panepistimioupoli B’, 45500 Ioannina, GreeceLaboratory of New Technologies and Distance Learning, Department of Early Childhood Education, School of Education, University of Ioannina, Panepistimioupoli, 45110 Ioannina, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 47150 Kostaki Artas, GreeceDevelopmental domains refer to different areas of a child’s growth and maturation, including physical, language, cognitive, and social–emotional skills. Understanding these domains helps parents, caregivers, and professionals track a child’s progress and identify potential areas of concern. Nevertheless, due to the high level of heterogeneity and overlap, neurodevelopmental disorders may go undiagnosed in children for a crucial period. Detecting neurodevelopmental disorders at an early stage is fundamental. Digital tools like artificial intelligence can help clinicians with the early detection process. To achieve this, a new method has been proposed that creates artificial features from the original ones derived from the SmartSpeech project, using a feature construction procedure guided by the Grammatical Evolution technique. The new features from a machine learning model are used to predict neurodevelopmental disorders. Comparative experiments demonstrated that using the feature creation method outperformed other machine learning methods for predicting neurodevelopmental disorders. In many cases, the reduction in the test error reaches up to 65% to the next better one.https://www.mdpi.com/2076-3417/14/1/305neurodevelopmental disordersscreeningfeature constructiongrammatical evolutionevolutionary techniques |
| spellingShingle | Eugenia I. Toki Giorgos Tatsis Jenny Pange Ioannis G. Tsoulos Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution Applied Sciences neurodevelopmental disorders screening feature construction grammatical evolution evolutionary techniques |
| title | Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution |
| title_full | Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution |
| title_fullStr | Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution |
| title_full_unstemmed | Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution |
| title_short | Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution |
| title_sort | constructing features for screening neurodevelopmental disorders using grammatical evolution |
| topic | neurodevelopmental disorders screening feature construction grammatical evolution evolutionary techniques |
| url | https://www.mdpi.com/2076-3417/14/1/305 |
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