ADHD screening tool: investigating the effectiveness of a tablet-based game with machine learning

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dc.contributor.advisorFourie, Pieter Rousseauen_ZA
dc.contributor.advisorVan den Heever, David Jacobusen_ZA
dc.contributor.authorSwarts, Romanoen_ZA
dc.contributor.otherStellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering (CRSES)en_ZA
dc.date.accessioned2019-02-28T10:29:40Z
dc.date.accessioned2019-04-17T08:35:55Z
dc.date.available2019-02-28T10:29:40Z
dc.date.available2019-04-17T08:35:55Z
dc.date.issued2019-04
dc.descriptionThesis (MEng)--Stellenbosch University, 2019.en_ZA
dc.description.abstractENGLISH ABSTRACT: This study investigated the effectiveness of a tablet-based game that incorporated machine learning to screen participants between the ages of six and twelve years for ADHD inattentive subtype. Prior to the design and development of the ADHD screening tool, a thorough investigation of the literature was conducted. Additionally, existing ADHD screening tools and cognitive training tools were identified. This research project implemented lessons learned from the literature, as well as input from medical professionals and the DSM-V diagnostic criteria. The ADHD screening tool presents a patient-testing interface in the form of a tablet-based game with a cloud-based machine learning classifier. The cloud-based classifier is integrated with an algorithm, and together they can discriminate between ADHD and non-ADHD patients with a sensitivity of 100i% and specificity of 87.5i%. The device used for testing was a single, internet connected, commercially available tablet. No additional hardware is required.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Hierdie studie het ondersoek ingestel om die effektiwiteit van 'n tablet-gebaseerde speletjie om deelnemers tussen die ouderdomme van ses en twaalf jaar vir ADHD-onoplettende subtipe te evalueer. Voor die ontwerp en ontwikkeling van die ADHD keuring instrument was 'n deeglike ondersoek ingestel om die literatuur te ondersoek. Daarbenewens was die bestaande ADHD keuring instrumente en kognitiewe opleidingsinstrumente geïdentifiseer. Hierdie navorsingsprojek het lesse van uit die literatuur geïmplementeer, sowel as insette van mediese professionele en die DSM-V diagnostiese kriteria. Die ADHD evalueringsinstrument bied 'n pasiënt-toets in die vorm van 'n tablet-gebaseerde speletjie met 'n wolk-gebaseerde masjienleer klassifiseerder. Die wolk-gebaseerde klassifiseerder is geïntegreer met 'n algoritme, en saam kan hulle onderskei tussen ADHD en nie-ADHD pasiënte met 'n sensitiwiteit van 100i% en spesifisiteit van 87.5i%. Die toestel wat gebruik was vir toetsing is 'n enkele, internet-gekoppelde, kommersieel beskikbare tablet. Geen bykomende hardeware word benodig nie.af_ZA
dc.format.extentxvii, 114 pages : illustrationsen_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/106231
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectADHD (Child behavior disorder)en_ZA
dc.subjectMachine learningen_ZA
dc.subjectAttention-deficit disordered childrenen_ZA
dc.subjectHyperactive childrenen_ZA
dc.subjectAttention-Deficit Hyperactivity Disorderen_ZA
dc.subjectADHD -- Treatmenten_ZA
dc.subjectADHD -- Diagnosisen_ZA
dc.subjectHuman-computer interactionen_ZA
dc.subjectComputer-human interactionen_ZA
dc.subjectUCTD
dc.titleADHD screening tool: investigating the effectiveness of a tablet-based game with machine learningen_ZA
dc.typeThesisen_ZA
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