Korean Journal of Biological Psychiatry (생물정신의학)
- Volume 12 Issue 2
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- Pages.165-172
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- 2005
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- 1225-8709(pISSN)
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- 2005-7571(eISSN)
Comparison between Logistic Regression and Artificial Neural Networks as MMPI Discriminator
MMPI 분석도구로서 인공신경망 분석과 로지스틱 회귀분석의 비교
- Lee, Jaewon (Department of Biosystems, Korea Advanced Institute of Science & Technology(KAIST)) ;
- Jeong, Bum Seok (Department of Neuropsychiatry, School of Medicine, Eulji University) ;
- Kim, Mi Sug (Department of Psychiatry, Gongju National Hospital) ;
- Choi, Jee Wook (Department of Psychiatry, School of Medicine, Catholic University, Daejeon Sungmo Hospital) ;
- Ahn, Byung Un (Department of Psychiatry, Gongju National Hospital)
- 이재원 (한국과학기술원 바이오시스템학과) ;
- 정범석 (을지대학교 의과대학 을지대학교병원 정신과학교실) ;
- 김미숙 (국립공주병원 정신과) ;
- 최지욱 (가톨릭대학교 의과대학 대전성모병원 정신과학교실) ;
- 안병은 (국립공주병원 정신과)
- Published : 2005.11.30
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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