Abstract
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.
Original language | English |
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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 |
Publisher | IOS Press |
Pages | 1447-1448 |
Number of pages | 2 |
Volume | 264 |
ISBN (Electronic) | 9781643680033 |
ISBN (Print) | 9781643680026 |
Publication status | Published - 2019 |
Event | 17th World Congress on Medical and Health Informatics: MEDINFO 2019 - Lyon, France Duration: 25 Aug 2019 → 30 Aug 2019 http://medinfo-lyon.org/en/about-us/medinfo2019/ |
Publication series
Name | Studies in Health Technology and Informatics |
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Publisher | IOS Press |
Volume | 264 |
ISSN (Print) | 0926-9630 |
ISSN (Electronic) | 1879-8365 |
Conference
Conference | 17th World Congress on Medical and Health Informatics |
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Country | France |
City | Lyon |
Period | 25/08/19 → 30/08/19 |
Internet address |