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Autism spectrum disorder (ASD) is a brain development disorder that restricts a person’s communication abilities and social interaction capabilities from natural growth. In this paper, we have applied various supervised classificationtechniques to detect the presence of child autism. Our findings show that the Sequential Minimal Optimization (SMO) classifier performs best to detect ASD cases with the highest accuracy and minimum execution time and error rate. We also identify the most dominant features in dectecting child autism.
|Title of host publication||17th World Congress of Medical and Health Informatics|
|Subtitle of host publication||Health and wellbeing E-networks for all|
|Editors||Lucila Ohno-Machado, Brigitte Seroussi|
|Place of Publication||Netherlands|
|Number of pages||2|
|Publication status||Published - 2019|
|Event||17th World Congress on Medical and Health Informatics: MEDINFO 2019 - Lyon, France|
Duration: 25 Aug 2019 → 30 Aug 2019
|Name||Studies in Health Technology and Informatics|
|Conference||17th World Congress on Medical and Health Informatics|
|Period||25/08/19 → 30/08/19|
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- 1 Internal HDR Supervision
Honours Dissertation on "Detecting Autism Spectrum Disorder using Machine Learning"
Ashad Kabir (Principal Supervisor)2019 → 2020
Activity: Supervision/Examination/Mentoring › Internal HDR Supervision