Detection of autism spectrum disorder (ASD) in children and adults using machine learning
Abstract Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can pr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-35910-1 |
| _version_ | 1827923031256727552 |
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| author | Muhammad Shoaib Farooq Rabia Tehseen Maidah Sabir Zabihullah Atal |
| author_facet | Muhammad Shoaib Farooq Rabia Tehseen Maidah Sabir Zabihullah Atal |
| author_sort | Muhammad Shoaib Farooq |
| collection | DOAJ |
| description | Abstract Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults). |
| first_indexed | 2024-03-13T04:50:51Z |
| format | Article |
| id | doaj.art-c410cf58d72341e0a879307562fe6086 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T04:50:51Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c410cf58d72341e0a879307562fe60862023-06-18T11:13:56ZengNature PortfolioScientific Reports2045-23222023-06-0113111310.1038/s41598-023-35910-1Detection of autism spectrum disorder (ASD) in children and adults using machine learningMuhammad Shoaib Farooq0Rabia Tehseen1Maidah Sabir2Zabihullah Atal3Department of Artificial Intelligence, University of Management and TechnologyDepartment of Computer Science, University of Central PunjabDepartment of Artificial Intelligence, University of Management and TechnologyDepartment of Computer Science, Kardan UniversityAbstract Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).https://doi.org/10.1038/s41598-023-35910-1 |
| spellingShingle | Muhammad Shoaib Farooq Rabia Tehseen Maidah Sabir Zabihullah Atal Detection of autism spectrum disorder (ASD) in children and adults using machine learning Scientific Reports |
| title | Detection of autism spectrum disorder (ASD) in children and adults using machine learning |
| title_full | Detection of autism spectrum disorder (ASD) in children and adults using machine learning |
| title_fullStr | Detection of autism spectrum disorder (ASD) in children and adults using machine learning |
| title_full_unstemmed | Detection of autism spectrum disorder (ASD) in children and adults using machine learning |
| title_short | Detection of autism spectrum disorder (ASD) in children and adults using machine learning |
| title_sort | detection of autism spectrum disorder asd in children and adults using machine learning |
| url | https://doi.org/10.1038/s41598-023-35910-1 |
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