A new computational intelligence approach to detect autistic features for autism screening

dc.contributor.author Thabtah, Fadi
dc.contributor.author Kamalov, Firuz
dc.contributor.author Rajab, Khairan
dc.date.accessioned 2021-03-24T06:47:01Z
dc.date.available 2021-03-24T06:47:01Z
dc.date.copyright © 2018
dc.date.issued 2018-09
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in International Journal of Medical Informatics (2018), available online at:https://doi.org/10.1016/j.ijmedinf.2018.06.009 en_US
dc.description.abstract Autism Spectrum Disorder (ASD) is one of the fastest growing developmental disability diagnosis. General practitioners (GPs) and family physicians are typically the first point of contact for patients or family members concerned with ASD traits observed in themselves or their family member. Unfortunately, some families and adult patients are unaware of ASD traits that may be exhibited and as a result do not seek out necessary diagnostic services or contact their GP. Therefore, providing a quick, accessible, and simple tool utilizing items related to ASD to these families may increase the likelihood they will seek professional assessment and is vital to the early detection and treatment of ASD. This study aims at identifying fewer, albeit influential, features in common ASD screening methods in order to achieve efficient screening as demands on evaluating the items’ influences on ASD within existing tools is urgent. To achieve this aim, a computational intelligence method called Variable Analysis (Va) is proposed that considers feature-to-class correlations and reduces feature-to-feature correlations. The results of the Va have been verified using two machine learning algorithms by deriving automated classification systems with respect to specificity, sensitivity, positive predictive values (PPVs), negative predictive values (NPVs), and predictive accuracy. Experimental results using cases and controls related to items in three common screening methods, along with features related to individuals, have been analysed and compared with results obtained from other common filtering methods. The results exhibited that Va was able to derive fewer numbers of features from adult, adolescent, and child screening methods yet maintained competitive predictive accuracy, sensitivity, and specificity rates. © 2018 Elsevier B.V. en_US
dc.identifier.citation Thabtah, F., Kamalov, F., & Rajab, K. (2018). A new computational intelligence approach to detect autistic features for autism screening. International Journal of Medical Informatics, 117, 112–124. https://doi.org/10.1016/j.ijmedinf.2018.06.009 en_US
dc.identifier.issn 13865056
dc.identifier.uri http://dx.doi.org/10.1016/j.ijmedinf.2018.06.009
dc.identifier.uri http://hdl.handle.net/20.500.12519/357
dc.language.iso en en_US
dc.publisher Elsevier Ireland Ltd en_US
dc.relation Authors Affiliations : Thabtah, F., Health and Human Sciences, Department of Psychology, University of Huddersfield, Huddersfield, United Kingdom; Kamalov, F., Canadian University Dubai, Dubai, United Arab Emirates; Rajab, K., College of Computer Science and Information System, Najran University, Najran, Saudi Arabia
dc.relation.ispartofseries International Journal of Medical Informatics; Volume 117
dc.rights License to reuse the abstract has been secured from Elsevier and Copyright Clearance Center.
dc.rights.holder Copyright : © 2018 Elsevier B.V.
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dc.subject Artificial intelligence en_US
dc.subject Behavioral research en_US
dc.subject Classifiers en_US
dc.subject Data mining en_US
dc.subject Diseases en_US
dc.subject Learning algorithms en_US
dc.subject Learning systems en_US
dc.subject Accuracy en_US
dc.subject Autism spectrum disorders en_US
dc.subject Behaviour science en_US
dc.subject Feature analysis en_US
dc.subject Sensitivity en_US
dc.subject Specificity en_US
dc.subject Diagnosis en_US
dc.title A new computational intelligence approach to detect autistic features for autism screening en_US
dc.type Article en_US
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