A clustering approach for autistic trait classification

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dc.contributor.authorBaadel, Said
dc.contributor.authorThabtah, Fadi
dc.contributor.authorLu, Joan
dc.date.accessioned2020-08-24T09:54:04Z
dc.date.available2020-08-24T09:54:04Z
dc.date.copyright© 2020
dc.date.issued2020-07-02
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article paper is published in Informatics for Health and Social Care (2020), available online at: https://doi.org/10.1080/17538157.2019.1687482en_US
dc.description.abstractMachine learning (ML) techniques can be utilized by physicians, clinicians, as well as other users, to discover Autism Spectrum Disorder (ASD) symptoms based on historical cases and controls to enhance autism screening efficiency and accuracy. The aim of this study is to improve the performance of detecting ASD traits by reducing data dimensionality and eliminating redundancy in the autism dataset. To achieve this, a new semi-supervised ML framework approach called Clustering-based Autistic Trait Classification (CATC) is proposed that uses a clustering technique and that validates classifiers using classification techniques. The proposed method identifies potential autism cases based on their similarity traits as opposed to a scoring function used by many ASD screening tools. Empirical results on different datasets involving children, adolescents, and adults were verified and compared to other common machine learning classification techniques. The results showed that CATC offers classifiers with higher predictive accuracy, sensitivity, and specificity rates than those of other intelligent classification approaches such as Artificial Neural Network (ANN), Random Forest, Random Trees, and Rule Induction. These classifiers are useful as they are exploited by diagnosticians and other stakeholders involved in ASD screening. © 2020 Taylor & Francis Group, LLC.en_US
dc.identifier.citationBaadel, S., Thabtah, F., & Lu, J. (2020). A clustering approach for autistic trait classification. Informatics for Health and Social Care, 45(3), 309-326, https://doi.org/10.1080/17538157.2019.1687482en_US
dc.identifier.issn17538157
dc.identifier.urihttps://doi.org/10.1080/17538157.2019.1687482
dc.identifier.urihttp://hdl.handle.net/20.500.12519/231
dc.language.isoenen_US
dc.publisherTaylor and Francis Ltden_US
dc.relationAuthors Affiliations : Baadel, S., Faculty of Engineering and Computing Science, University of Huddersfield, Huddersfield, United Kingdom, Faculty of Communication, Arts and Sciences, Canadian University Dubai, Dubai, United Arab Emirates; Thabtah, F., Dept of Digital Technologies, Manukau Institute of Technology, Manukau, New Zealand; Lu, J., Faculty of Engineering and Computing Science, University of Huddersfield, Huddersfield, United Kingdom
dc.relation.ispartofseriesInformatics for Health and Social Care;Volume 45, Issue 3
dc.rightsPermission to reuse the abstract has been secured from Taylor & Francis and Copyright Clearance Center..
dc.rights.holderCopyright : © 2020 Taylor & Francis Group, LLC.
dc.subjectAutismen_US
dc.subjectAutistic Disorderen_US
dc.subjectToddlersen_US
dc.subjectAutism diagnosisen_US
dc.subjectclassificationen_US
dc.subjectclusteringen_US
dc.subjectmachine learningen_US
dc.subjectOMCOKEen_US
dc.subjectpredictive modelsen_US
dc.titleA clustering approach for autistic trait classificationen_US
dc.typeArticleen_US

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