Journal of Physiology & Pathology in Korean Medicine (동의생리병리학회지)

The Physiological Society of Korean Medicine and The Society of Pathology in Korean Medicine (대한동의생리학회·한의병리학회)
권호 표지

Development of Sasang Type Diagnostic Test with Neural Network

신경망을 사용한 사상체질 진단검사 개발 연구

  • Chae, Han (Division of Longevity and Biofunctional Medicine, School of Korean Medicine, Pusan National University) ;
  • Hwang, Sang-Moon (Division of Longevity and Biofunctional Medicine, School of Korean Medicine, Pusan National University) ;
  • Eom, Il-Kyu (School of Korean Medicine, Pusan National University) ;
  • Kim, Byoung-Chul (Department of Biomedical Engineering, College of Natural Resource and Life Science, Pusan National University) ;
  • Kim, Young-In (Department of Biomedical Engineering, College of Natural Resource and Life Science, Pusan National University) ;
  • Kim, Byung-Joo (Division of Longevity and Biofunctional Medicine, School of Korean Medicine, Pusan National University) ;
  • Kwon, Young-Kyu (Division of Longevity and Biofunctional School of Electrical Engineering, Pusan National University)
  • 채한 (부산대학교 한의학전문대학원 양생기능의학부) ;
  • 황상문 (부산대학교 한의학전문대학원 양생기능의학부) ;
  • 엄일규 (부산대학교 전자전기공학부) ;
  • 김병철 (부산대학교 생명자원과학대학 바이오메디컬공학과) ;
  • 김영인 (부산대학교 생명자원과학대학 바이오메디컬공학과) ;
  • 김병주 (부산대학교 한의학전문대학원 양생기능의학부) ;
  • 권영규 (부산대학교 한의학전문대학원 양생기능의학부)
  • Published : 2009.08.25
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Abstract

The medical informatics for clustering Sasang types with collected clinical data is important for the personalized medicine, but it has not been thoroughly studied yet. The purpose of this study was to examine the usefulness of neural network data mining algorithm for traditional Korean medicine. We used Kohonen neural network, the Self-Organizing Map (SOM), for the analysis of biomedical information following data pre-processing and calculated the validity index as percentage correctly predicted and type-specific sensitivity. We can extract 12 data fields from 30 after data pre-processing with correlation analysis and latent functional relationship analysis. The profile of Myers-Briggs Type Inidcator and Bio-Impedance Analysis data which are clustered with SOM was similar to that of original measurements. The percentage correctly predicted was 56%, and sensitivity for So-Yang, Tae-Eum and So-Eum type were 56%, 48%, and 61%, respectively. This study showed that the neural network algorithm for clustering Sasang types based on clinical data is useful for the sasang type diagnostic test itself. We discussed the importance of data pre-processing and clustering algorithm for the validity of medical devices in traditional Korean medicine.

Keywords

References

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