Early Detection of Autism Spectrum Disorder Through Automated Machine Learning
<b>Background/Objectives</b>: Autism spectrum disorder (ASD) is a neurodevelopmental disorder distinguished by an extensive range of symptoms, including reduced social interaction, communication difficulties and tiresome behaviors. Early detection of ASD is important because it allows fo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/15/1859 |
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| author | Khafsa Ehsan Kashif Sultan Abreen Fatima Muhammad Sheraz Teong Chee Chuah |
| author_facet | Khafsa Ehsan Kashif Sultan Abreen Fatima Muhammad Sheraz Teong Chee Chuah |
| author_sort | Khafsa Ehsan |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: Autism spectrum disorder (ASD) is a neurodevelopmental disorder distinguished by an extensive range of symptoms, including reduced social interaction, communication difficulties and tiresome behaviors. Early detection of ASD is important because it allows for timely intervention, which significantly improves developmental, behavioral, and communicative outcomes in children. However, traditional diagnostic procedures for identifying autism spectrum disorder (ASD) typically involve lengthy clinical examinations, which can be both time-consuming and costly. This research proposes leveraging automated machine learning (AUTOML) to streamline the diagnostic process and enhance its accuracy. <b>Methods</b>: In this study, by collecting data from various rehabilitation centers across Pakistan, we applied a specific AUTOML tool known as Tree-based Pipeline Optimization Tool (TPOT) for ASD detection. Notably, this study marks one of the initial explorations into utilizing AUTOML for ASD detection. The experimentations indicate that the TPOT provided the best pipeline for the dataset, which was verified using a manual machine learning method. <b>Results</b>: The study contributes to the field of ASD diagnosis by using AUTOML to determine the likelihood of ASD in children at prompt stages of evolution. The study also provides an evaluation of precision, recall, and F1-score metrics to confirm the correctness of the diagnosis. The propose TPOT-based AUTOML framework attained an overall accuracy 78%, with a precision of 83%, a recall of 90%, and an F1-score of 86% for the autistic class. <b>Conclusions</b>: In summary, this research offers an encouraging approach to improve the detection of autism spectrum disorders (ASD) in children, which could lead to better results for affected individuals and their families. |
| format | Article |
| id | doaj-art-afdba83b73e149eba727ce7a10875002 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-afdba83b73e149eba727ce7a108750022025-08-20T03:36:03ZengMDPI AGDiagnostics2075-44182025-07-011515185910.3390/diagnostics15151859Early Detection of Autism Spectrum Disorder Through Automated Machine LearningKhafsa Ehsan0Kashif Sultan1Abreen Fatima2Muhammad Sheraz3Teong Chee Chuah4Department of Software Engineering, Bahria University Islamabad, Islamabad 44000, PakistanDepartment of Software Engineering, Bahria University Islamabad, Islamabad 44000, PakistanDepartment of Professional Psychology, National University of Modern Languages, Islamabad 44000, PakistanCentre for Wireless Technology, Faculty of Engineering, Multimedia University, Cyberjaya 63100, Selangor, MalaysiaCentre for Wireless Technology, Faculty of Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia<b>Background/Objectives</b>: Autism spectrum disorder (ASD) is a neurodevelopmental disorder distinguished by an extensive range of symptoms, including reduced social interaction, communication difficulties and tiresome behaviors. Early detection of ASD is important because it allows for timely intervention, which significantly improves developmental, behavioral, and communicative outcomes in children. However, traditional diagnostic procedures for identifying autism spectrum disorder (ASD) typically involve lengthy clinical examinations, which can be both time-consuming and costly. This research proposes leveraging automated machine learning (AUTOML) to streamline the diagnostic process and enhance its accuracy. <b>Methods</b>: In this study, by collecting data from various rehabilitation centers across Pakistan, we applied a specific AUTOML tool known as Tree-based Pipeline Optimization Tool (TPOT) for ASD detection. Notably, this study marks one of the initial explorations into utilizing AUTOML for ASD detection. The experimentations indicate that the TPOT provided the best pipeline for the dataset, which was verified using a manual machine learning method. <b>Results</b>: The study contributes to the field of ASD diagnosis by using AUTOML to determine the likelihood of ASD in children at prompt stages of evolution. The study also provides an evaluation of precision, recall, and F1-score metrics to confirm the correctness of the diagnosis. The propose TPOT-based AUTOML framework attained an overall accuracy 78%, with a precision of 83%, a recall of 90%, and an F1-score of 86% for the autistic class. <b>Conclusions</b>: In summary, this research offers an encouraging approach to improve the detection of autism spectrum disorders (ASD) in children, which could lead to better results for affected individuals and their families.https://www.mdpi.com/2075-4418/15/15/1859autism spectrum disorderAUTOMLTPOT |
| spellingShingle | Khafsa Ehsan Kashif Sultan Abreen Fatima Muhammad Sheraz Teong Chee Chuah Early Detection of Autism Spectrum Disorder Through Automated Machine Learning Diagnostics autism spectrum disorder AUTOML TPOT |
| title | Early Detection of Autism Spectrum Disorder Through Automated Machine Learning |
| title_full | Early Detection of Autism Spectrum Disorder Through Automated Machine Learning |
| title_fullStr | Early Detection of Autism Spectrum Disorder Through Automated Machine Learning |
| title_full_unstemmed | Early Detection of Autism Spectrum Disorder Through Automated Machine Learning |
| title_short | Early Detection of Autism Spectrum Disorder Through Automated Machine Learning |
| title_sort | early detection of autism spectrum disorder through automated machine learning |
| topic | autism spectrum disorder AUTOML TPOT |
| url | https://www.mdpi.com/2075-4418/15/15/1859 |
| work_keys_str_mv | AT khafsaehsan earlydetectionofautismspectrumdisorderthroughautomatedmachinelearning AT kashifsultan earlydetectionofautismspectrumdisorderthroughautomatedmachinelearning AT abreenfatima earlydetectionofautismspectrumdisorderthroughautomatedmachinelearning AT muhammadsheraz earlydetectionofautismspectrumdisorderthroughautomatedmachinelearning AT teongcheechuah earlydetectionofautismspectrumdisorderthroughautomatedmachinelearning |