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Korean J Biol Psychiatry. 2005 Nov;12(2):165-172. Korean. Original Article.
Lee J , Jeong BS , Kim MS , Choi JW , Ahn BU .
Department of Biosystems, Korea Advanced Institute of Science & Technology(KAIST), Daejeon, Korea. jaewon@raphe.kaist.ac.kr
Department of Psychiatry, Gongju National Hospital, Gongju, Korea.
Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon, Korea.
Department of Psychiatry, School of Medicine, Catholic University, Daejeon Sungmo Hospital, Daejeon, Korea.
Abstract

OBJECTIVES: The purpose of this study is to 1) conduct a discrimination analysis of schizophrenia and bipolar affective disorder using MMPI profile through artificial neural network analysis and logistic regression analysis, 2) to make a comparison between advantages and disadvantages of the two methods, and 3) to demonstrate the usefulness of artificial neural network analysis of psychiatric data. PROCEDURE: The MMPI profiles for 181 schizophrenia and bipolar affective disorder patients were selected. Of these profiles, 50 were randomly placed in the learning group and the remaining 131 were placed in the validation group. The artificial neural network was trained using the profiles of the learning group and the 131 profiles of the validation group were analyzed. A logistic regression analysis was then conducted in a similar manner. The results of the two analyses were compared and contrasted using sensitivity, specificity, ROC curves, and kappa index. RESULTS: Logistic regression analysis and artificial neural network analysis both exhibited satisfactory discriminating ability at Kappa index of greater than 0.4. The comparison of the two methods revealed artificial neural network analysis is superior to logistic regression analysis in its discriminating capacity, displaying higher values of Kappa index, specificity, and AUC(Area Under the Curve) of ROC curve than those of logistic regression analysis. CONCLUSION: Artificial neural network analysis is a new tool whose frequency of use has been increasing for its superiority in nonlinear applications. However, it does possess insufficiencies such as difficulties in understanding the relationship between dependent and independent variables. Nevertheless, when used in conjunction with other analysis tools which supplement it, such as the logistic regression analysis, it may serve as a powerful tool for psychiatric data analysis.

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