Using diagnostic information to develop a machine learning application for the effective screening of autism spectrum disorders

Goh, Tze Jui, Diederich, Joachim, Song, Insu, and Sung, Min (2014) Using diagnostic information to develop a machine learning application for the effective screening of autism spectrum disorders. In: Lech, Margaret, Song, Insu, Yellowlees, Peter, and Diederich, Joachim, (eds.) Mental Health Informatics. Studies in Computational Intelligence, 491 . Springer, Berlin, Germany, pp. 229-245.

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Abstract

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.

Item ID: 30714
Item Type: Book Chapter (Research - B1)
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ISBN: 978-3-642-38549-0
ISSN: 1860-9503
Date Deposited: 01 Apr 2014 01:31
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing @ 50%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 50%
SEO Codes: 92 HEALTH > 9202 Health and Support Services > 920203 Diagnostic Methods @ 50%
92 HEALTH > 9202 Health and Support Services > 920209 Mental Health Services @ 50%
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