Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1920
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWingfield, B-
dc.contributor.authorMiller, S-
dc.contributor.authorYogarajah, P-
dc.contributor.authorKerr, D-
dc.contributor.authorGardiner, B-
dc.contributor.authorSeneviratne, S-
dc.contributor.authorSamarasinghe, P-
dc.contributor.authorColeman, S-
dc.date.accessioned2022-04-06T08:57:16Z-
dc.date.available2022-04-06T08:57:16Z-
dc.date.issued2020-12-
dc.identifier.issn2538–2553-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1920-
dc.description.abstractAutism spectrum disorder is an umbrella term for a group of neurodevelopmental disorders that is associated with impairments to social interaction, communication, and behaviour. Typically, autism spectrum disorder is first detected with a screening tool (e.g. modified checklist for autism in toddlers). However, the interpretation of autism spectrum disorder behavioural symptoms varies across cultures: the sensitivity of modified checklist for autism in toddlers is as low as 25 per cent in Sri Lanka. A culturally sensitive screening tool called pictorial autism assessment schedule has overcome this problem. Low- and middle-income countries have a shortage of mental health specialists, which is a key barrier for obtaining an early autism spectrum disorder diagnosis. Early identification of autism spectrum disorder enables intervention before atypical patterns of behaviour and brain function become established. This article proposes a culturally sensitive autism spectrum disorder screening mobile application. The proposed application embeds an intelligent machine learning model and uses a clinically validated symptom checklist to monitor and detect autism spectrum disorder in low- and middle-income countries for the first time. Machine learning models were trained on clinical pictorial autism assessment schedule data and their predictive performance was evaluated, which demonstrated that the random forest was the optimal classifier (area under the receiver operating characteristic (0.98)) for embedding into the mobile screening tool. In addition, feature selection demonstrated that many pictorial autism assessment schedule questions are redundant and can be removed to optimise the screening process.en_US
dc.language.isoenen_US
dc.publisherSAGE Publicationsen_US
dc.relation.ispartofseriesHealth informatics journal;Vol 26 Issue 4 Pages 2538-2553-
dc.subjectautism spectrum disorderen_US
dc.subjectdecision support systemen_US
dc.subjectmachine learningen_US
dc.titleA predictive model for paediatric autism screeningen_US
dc.typeArticleen_US
dc.identifier.doidoi.org/10.1177/1460458219887823en_US
Appears in Collections:Department of Information Technology-Scopes
Research Papers - Open Access Research
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

Files in This Item:
File Description SizeFormat 
1460458219887823.pdf480.24 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.