Using diagnostic information to develop a machine learning application for the effective screening of autism spectrum disorders
Other Publication ResearchOnline@JCUAbstract
A 2-Class Support Vector Machine (SVM) classification model was developed by means of machine learning techniques and text analysis of Autism Spectrum Disorders (ASD) diagnostic reports. The ability of the 2-Class SVM application to screen for ASD is compared with other screening instruments: Gillian Autism Rating Scale—Second Edition [25], Social Communication Questionnaire [51] and Social Responsiveness Scale [11]. It was also cross-validated and refined based on a sample (n = 221). The classification performance of the SVM application was relatively better compared to the other instruments (accuracy = 83.7 %, precision = 98.8 %, sensitivity = 83.3 %, specificity = 88.9 %). A 1-Class SVM classification model was also described to highlight the usefulness of SVM with a skewed population.
Journal
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Publication Name
Mental Health Informatics
Volume
491
ISBN/ISSN
1860-9503
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Pages Count
17
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Publisher
Springer
Publisher Url
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Publisher Location
Berlin, Germany
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EISSN
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DOI
10.1007/978-3-642-38550-6_13