Utilizing Constructed Neural Networks for Autism Screening
Autism Spectrum Disorder is known to cause difficulties in social interaction and communication, as well as repetitive patterns of behavior, interests, or hobbies. These challenges can significantly affect the individual’s daily life. Therefore, it is crucial to identify and assess children with Aut...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/7/3053 |
| _version_ | 1797212820642725888 |
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| author | Eugenia I. Toki Jenny Pange Giorgos Tatsis Konstantinos Plachouras Ioannis G. Tsoulos |
| author_facet | Eugenia I. Toki Jenny Pange Giorgos Tatsis Konstantinos Plachouras Ioannis G. Tsoulos |
| author_sort | Eugenia I. Toki |
| collection | DOAJ |
| description | Autism Spectrum Disorder is known to cause difficulties in social interaction and communication, as well as repetitive patterns of behavior, interests, or hobbies. These challenges can significantly affect the individual’s daily life. Therefore, it is crucial to identify and assess children with Autism Spectrum Disorder early to significantly benefit the long-term health of children. Unfortunately, many children are not diagnosed or are misdiagnosed, which means they miss out on the necessary interventions. Clinicians and other experts face various challenges during the diagnostic process. Digital tools can facilitate early diagnosis effectively. This study aimed to explore the use of machine learning techniques on a dataset collected from a serious game designed for children with autism to investigate how these techniques can assist in classification and make the clinical process more efficient. The responses were gathered from children who participated in interactive games deployed on mobile devices, and the data were analyzed using various types of neural networks, such as multilayer perceptrons and constructed neural networks. The performance metrics of these models, including error rate, precision, and recall, were reported, and the comparative experiments revealed that the constructed neural network using the integer rule-based neural networks approach was superior. Based on the evaluation metrics, this method showed the lowest error rate of 11.77%, a high accuracy of 0.75, and a good recall of 0.66. Thus, it can be an effective way to classify both typically developed children and children with Autism Spectrum Disorder. Additionally, it can be used for automatic screening procedures in an intelligent system. The results indicate that clinicians could use these techniques to enhance conventional screening methods and contribute to providing better care for individuals with autism. |
| first_indexed | 2024-04-24T10:48:28Z |
| format | Article |
| id | doaj.art-10cb925d930846b2b8f8917ce41f5b66 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T10:48:28Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-10cb925d930846b2b8f8917ce41f5b662024-04-12T13:15:33ZengMDPI AGApplied Sciences2076-34172024-04-01147305310.3390/app14073053Utilizing Constructed Neural Networks for Autism ScreeningEugenia I. Toki0Jenny Pange1Giorgos Tatsis2Konstantinos Plachouras3Ioannis G. Tsoulos4Department of Speech and Language Therapy, School of Health Sciences, University of Ioannina, 45500 Ioannina, GreeceLaboratory of New Technologies and Distance Learning, Department of Early Childhood Education, School of Education, University of Ioannina, 45110 Ioannina, GreeceDepartment of Speech and Language Therapy, School of Health Sciences, University of Ioannina, 45500 Ioannina, GreeceDepartment of Speech and Language Therapy, School of Health Sciences, University of Ioannina, 45500 Ioannina, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 47150 Kostaki Artas, GreeceAutism Spectrum Disorder is known to cause difficulties in social interaction and communication, as well as repetitive patterns of behavior, interests, or hobbies. These challenges can significantly affect the individual’s daily life. Therefore, it is crucial to identify and assess children with Autism Spectrum Disorder early to significantly benefit the long-term health of children. Unfortunately, many children are not diagnosed or are misdiagnosed, which means they miss out on the necessary interventions. Clinicians and other experts face various challenges during the diagnostic process. Digital tools can facilitate early diagnosis effectively. This study aimed to explore the use of machine learning techniques on a dataset collected from a serious game designed for children with autism to investigate how these techniques can assist in classification and make the clinical process more efficient. The responses were gathered from children who participated in interactive games deployed on mobile devices, and the data were analyzed using various types of neural networks, such as multilayer perceptrons and constructed neural networks. The performance metrics of these models, including error rate, precision, and recall, were reported, and the comparative experiments revealed that the constructed neural network using the integer rule-based neural networks approach was superior. Based on the evaluation metrics, this method showed the lowest error rate of 11.77%, a high accuracy of 0.75, and a good recall of 0.66. Thus, it can be an effective way to classify both typically developed children and children with Autism Spectrum Disorder. Additionally, it can be used for automatic screening procedures in an intelligent system. The results indicate that clinicians could use these techniques to enhance conventional screening methods and contribute to providing better care for individuals with autism.https://www.mdpi.com/2076-3417/14/7/3053Autism Spectrum Disorder (ASD)screeningclassificationmachine learningconstructed neural networks |
| spellingShingle | Eugenia I. Toki Jenny Pange Giorgos Tatsis Konstantinos Plachouras Ioannis G. Tsoulos Utilizing Constructed Neural Networks for Autism Screening Applied Sciences Autism Spectrum Disorder (ASD) screening classification machine learning constructed neural networks |
| title | Utilizing Constructed Neural Networks for Autism Screening |
| title_full | Utilizing Constructed Neural Networks for Autism Screening |
| title_fullStr | Utilizing Constructed Neural Networks for Autism Screening |
| title_full_unstemmed | Utilizing Constructed Neural Networks for Autism Screening |
| title_short | Utilizing Constructed Neural Networks for Autism Screening |
| title_sort | utilizing constructed neural networks for autism screening |
| topic | Autism Spectrum Disorder (ASD) screening classification machine learning constructed neural networks |
| url | https://www.mdpi.com/2076-3417/14/7/3053 |
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