Machine Learning for Predicting Neurodevelopmental Disorders in Children
Developmental domains like physical, verbal, cognitive, and social-emotional skills are crucial for monitoring a child’s growth. However, identifying neurodevelopmental deficiencies can be challenging due to the high level of variability and overlap. Early detection is essential, and digital procedu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/2/837 |
| _version_ | 1797340139882545152 |
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| author | Eugenia I. Toki Ioannis G. Tsoulos Vito Santamato Jenny Pange |
| author_facet | Eugenia I. Toki Ioannis G. Tsoulos Vito Santamato Jenny Pange |
| author_sort | Eugenia I. Toki |
| collection | DOAJ |
| description | Developmental domains like physical, verbal, cognitive, and social-emotional skills are crucial for monitoring a child’s growth. However, identifying neurodevelopmental deficiencies can be challenging due to the high level of variability and overlap. Early detection is essential, and digital procedures can assist in the process. This study leverages the current advances in artificial intelligence to address the prediction of neurodevelopmental disorders through a comprehensive machine learning approach. A novel and recently developed serious game dataset, collecting various data on children’s speech and linguistic responses, was used. The initial dataset comprised 520 instances, reduced to 473 participants after rigorous data preprocessing. Cluster analysis revealed distinct patterns and structures in the data, while reliability analysis ensured measurement consistency. A robust prediction model was developed using logistic regression. Applied to a subset of 184 participants with an average age of 7 years, the model demonstrated high accuracy, precision, recall, and F1-score, effectively distinguishing between instances with and without neurodevelopmental disorders. In conclusion, this research highlights the effectiveness of the machine learning approach in diagnosing neurodevelopmental disorders based on cognitive features, and offers new opportunities for decision making, classification, and clinical assessment, paving the way for early and personalized interventions for at-risk individuals. |
| first_indexed | 2024-03-08T09:57:40Z |
| format | Article |
| id | doaj.art-460b59ebc6cd49fb92f0963b5a4255f6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:57:40Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-460b59ebc6cd49fb92f0963b5a4255f62024-01-29T13:45:10ZengMDPI AGApplied Sciences2076-34172024-01-0114283710.3390/app14020837Machine Learning for Predicting Neurodevelopmental Disorders in ChildrenEugenia I. Toki0Ioannis G. Tsoulos1Vito Santamato2Jenny Pange3Department of Speech and Language Therapy, School of Health Sciences, University of Ioannina, Panepistimioupoli B’, 45500 Ioannina, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 47150 Kostaki Artas, GreeceDepartment of Economics, University of Foggia, 1 Romolo Caggese Street, 71121 Foggia, FG, ItalyLaboratory of New Technologies and Distance Learning, Department of Early Childhood Education, School of Education, University of Ioannina, Panepistimioupoli, 45110 Ioannina, GreeceDevelopmental domains like physical, verbal, cognitive, and social-emotional skills are crucial for monitoring a child’s growth. However, identifying neurodevelopmental deficiencies can be challenging due to the high level of variability and overlap. Early detection is essential, and digital procedures can assist in the process. This study leverages the current advances in artificial intelligence to address the prediction of neurodevelopmental disorders through a comprehensive machine learning approach. A novel and recently developed serious game dataset, collecting various data on children’s speech and linguistic responses, was used. The initial dataset comprised 520 instances, reduced to 473 participants after rigorous data preprocessing. Cluster analysis revealed distinct patterns and structures in the data, while reliability analysis ensured measurement consistency. A robust prediction model was developed using logistic regression. Applied to a subset of 184 participants with an average age of 7 years, the model demonstrated high accuracy, precision, recall, and F1-score, effectively distinguishing between instances with and without neurodevelopmental disorders. In conclusion, this research highlights the effectiveness of the machine learning approach in diagnosing neurodevelopmental disorders based on cognitive features, and offers new opportunities for decision making, classification, and clinical assessment, paving the way for early and personalized interventions for at-risk individuals.https://www.mdpi.com/2076-3417/14/2/837neurodevelopmental disordersscreeningchildrenmachine learning predictionlogistic regression model |
| spellingShingle | Eugenia I. Toki Ioannis G. Tsoulos Vito Santamato Jenny Pange Machine Learning for Predicting Neurodevelopmental Disorders in Children Applied Sciences neurodevelopmental disorders screening children machine learning prediction logistic regression model |
| title | Machine Learning for Predicting Neurodevelopmental Disorders in Children |
| title_full | Machine Learning for Predicting Neurodevelopmental Disorders in Children |
| title_fullStr | Machine Learning for Predicting Neurodevelopmental Disorders in Children |
| title_full_unstemmed | Machine Learning for Predicting Neurodevelopmental Disorders in Children |
| title_short | Machine Learning for Predicting Neurodevelopmental Disorders in Children |
| title_sort | machine learning for predicting neurodevelopmental disorders in children |
| topic | neurodevelopmental disorders screening children machine learning prediction logistic regression model |
| url | https://www.mdpi.com/2076-3417/14/2/837 |
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