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 Margaret Lech, Peter Yellowlees, Insu Song and Joachim Diederich (Ed.), Mental health informatics (pp. 229-245) Heidelberg, Germany: Springer. doi:10.1007/978-3-642-38550-6_13


Author Goh, Tze Jui
Diederich, Joachim
Song, Insu
Sung, Min
Title of chapter Using diagnostic information to develop a machine learning application for the effective screening of autism spectrum disorders
Title of book Mental health informatics
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2014
Sub-type Article (original research)
DOI 10.1007/978-3-642-38550-6_13
Open Access Status Not yet assessed
Year available 2014
Series Studies in Computational Intelligence
ISBN 9783642385490
9783642385506
ISSN 1860-949X
1860-9503
Editor Margaret Lech
Peter Yellowlees
Insu Song
Joachim Diederich
Volume number 491
Chapter number 13
Start page 229
End page 245
Total pages 17
Total chapters 13
Language eng
Abstract/Summary 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.
Q-Index Code B1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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