Felin, Diederich, Joachim, and Song, Insu (2014) An alternative method of analysis in the absence of control group. In: Lech, Margaret, Song, Insu, Yellowlees, Peter, and Diederich, Joachim, (eds.) Mental Health Informatics. Studies in Computational Intelligence, 491 . Springer, Berlin, Germany, pp. 151-161.

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Abstract

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.

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