An AI Powered System to Detect Autism Spectrum Disorder in Toddlers
Conference paper
Amirhosseini, M. H., Alam, N., Kalabi, F. and Virdee, B. 2024. An AI Powered System to Detect Autism Spectrum Disorder in Toddlers. ICDAM-2024: 5th International Conference on Data Analytics and Management. London, UK 14 - 15 Jun 2024 Springer Nature. https://doi.org/10.1007/978-981-96-3352-4_50
Authors | Amirhosseini, M. H., Alam, N., Kalabi, F. and Virdee, B. |
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Type | Conference paper |
Abstract | The prevalence of autism has increased over time, yet the rate of its detection remains static, often due to the scarcity of staff and resources necessary for supporting and diagnosing children with autism. Leveraging the advancing field of computing, this study introduces a machine learning strategy to tackle this challenge. Unlike prior research which predominantly employed traditional machine learning methods to diagnose older age groups, our focus was on identifying autism in toddlers, assessing the effectiveness of both deep learning and traditional machine learning classifiers to achieve more accurate and efficient diagnostic outcomes. The dataset for this study, titled ‘Autistic Spectrum Disorder Screening for Toddlers,’ was compiled using the QCHAT-10 child assessment questionnaire. We examined five machine learning models including Support Vector Machine, XGBoost, Logistic Regression, Decision Tree, and Multi-Layer Perceptron—a feedforward artificial neural network. These models were assessed using metrics such as accuracy, recall, precision, and F1 score, and underwent 10-fold cross validation to ascertain consistent and dependable mean accuracy scores. The comparative analysis identified the Multi-Layer Perceptron model as the most effective, with an accuracy of 98%. This research provides a potential auxiliary tool for psychologists and a self-assessment resource for individuals, aiding in the early identification of autism. |
Year | 2024 |
Conference | ICDAM-2024: 5th International Conference on Data Analytics and Management |
Publisher | Springer Nature |
Accepted author manuscript | License File Access Level Anyone |
Publication dates | |
Online | 02 Jul 2025 |
Publication process dates | |
Completed | Jun 2024 |
Accepted | 27 Feb 2024 |
Deposited | 08 Oct 2024 |
Journal citation | p. 659–667 |
ISSN | 2367-3370 |
2367-3389 | |
Book title | Proceedings of Data Analytics and Management: ICDAM 2024, Volume 1 |
Book editor | Swaroop, A. |
Virdee, B. | |
Correia, S. D. | |
Polkowski, Z. | |
ISBN | 978-981-96-3351-7 |
978-981-96-3352-4 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-96-3352-4_50 |
Web address (URL) of conference proceedings | https://link.springer.com/book/10.1007/978-981-96-3352-4 |
Copyright holder | © 2024 The Authors |
https://repository.uel.ac.uk/item/8y550
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Accepted author manuscript
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