An alternative method of analysis in the absence of control group

Felin, Diederich, Joachim and Song, Insu (2014). An alternative method of analysis in the absence of control group. In Margaret Lech, Peter Yellowlees, Insu Song and Joachim Diederich (Ed.), Mental health informatics (pp. 151-161) Heidelberg, Germany: Springer. doi:10.1007/978-3-642-38550-6_8


Author Felin
Diederich, Joachim
Song, Insu
Title of chapter An alternative method of analysis in the absence of control group
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_8
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 8
Start page 151
End page 161
Total pages 11
Total chapters 13
Language eng
Abstract/Summary Although control groups are an important part of psychology, there are times when an appropriate control group is difficult to obtain. In the machine learning community, Support Vector Machine has often been successfully used for classification. Moreover, SVM can also be used for classification using data from one group of participants only, known as one-class SVM. In order to test the effectiveness of two-class and one-class SVMs, they were compared to TLC and CLANG in diagnosing disorganised speech. It was hypothesised that SVM would be as good as TLC and CLANG in diagnosing schizophrenic speech. It was also predicted that one-class SVM would perform just as well as two-class SVM in identifying schizophrenic speech. Lastly, it was predicted that the control group in this study would be a better match to the schizophrenic group compared to the control group studied in Chap. 11 by Tilaka. Method: 12 control group participants were each interviewed for about 20 min. The interviews were then rated for disorganised speech using TLC, CLANG, and SVM. Data for the schizophrenic group were obtained from Tilaka. Results: It was found that two-class SVM was as good as TLC and CLANG in diagnosing schizophrenic speech. It was also found that one-class SVM was comparable to two-class SVM in classifying schizophrenic speech. However, compared to the control group of Tilaka, the new control group was not a better match to the schizophrenic group. Conclusion: One-class and two-class SVMs appear to be a good alternative method of analysis.
Q-Index Code B1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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