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Development of Sasang Type Diagnostic Test with Neural Network  

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)
Publication Information
Journal of Physiology & Pathology in Korean Medicine / v.23, no.4, 2009 , pp. 765-771 More about this Journal
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
neural network; sasang type diagnostic test; data pre-processing; clustering algorithm; medical informatics;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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