Batsakis, S, Adamou, M, Tachmazidis, I et al. (4 more authors) (2022) Data-Driven Decision Support for Adult Autism Diagnosis Using Machine Learning. Digital, 2 (2). pp. 224-243. ISSN 2673-6470
Abstract
Adult referrals to specialist autism spectrum disorder diagnostic services have increased in recent years, placing strain on existing services and illustrating the need for the development of a reliable screening tool, in order to identify and prioritize patients most likely to receive an ASD diagnosis. In this work a detailed overview of existing approaches is presented and a data driven analysis using machine learning is applied on a dataset of adult autism cases consisting of 192 cases. Our results show initial promise, achieving total positive rate (i.e., correctly classified instances to all instances ratio) up to 88.5%, but also point to limitations of currently available data, opening up avenues for further research. The main direction of this research is the development of a novel autism screening tool for adults (ASTA) also introduced in this work and preliminary results indicate the ASTA is suitable for use as a screening tool for adult populations in clinical settings.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | machine learning; autism diagnosis; decision support |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Jul 2022 15:16 |
Last Modified: | 06 Jul 2022 15:16 |
Status: | Published |
Publisher: | MDPI AG |
Identification Number: | 10.3390/digital2020014 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188488 |