Self Health Diagnosis System with Korean Traditional Medicine Using Fuzzy ART and Fuzzy Inference Rules

• Published in: Intelligent Information and Database Systems pp. 326 - 335
• Main Authors: Kim, Kwang-Baek, Kim, Jin-Whan
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: fuzzy ART; Korean traditional medicine; self health diagnosis
• ISBN: 9783642284922; 3642284922
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-28493-9_35

Korean traditional medicine has obtained more attention from the public and IT service industry especially after ’Dong-eui-bo-gam’ was registered to UNESCO Memory of the World. However, there are many obstacles in developing and commercializing an on-line self-diagnosis system by Korean traditional medicine. From the service point of view, since people are accustomed to the westernized style of diagnosis (symptom-disease pair), it is not easy to understand what traditional Korean medicine diagnoses and how one can react. Technically speaking, we need a special symptom-disease database because Korean traditional medicine has been built upon the innate characteristics of Korean people’s body. Thus, in this paper, we propose a self-diagnosis system of Korean traditional medicine based on Korean Standard Causes of Death Disease Classification Index (KCD) and fuzzy ART/inference method. Since this is for self-diagnosis, our system has graphical user-friendly interface that accepts symptoms of user from a certain part of body where the user feels inconvenient. Then, fuzzy ART algorithm and fuzzy inference engine picks up five most probable diseases with their causes and treatments extracted from Korean traditional medicine books. The power of our system comes from a fuzzy inference module combined with fuzzy ART algorithm that helps classifying related disease from database with accuracy. Our system is verified by field experts of Korean traditional medicine in collecting symptom-disease-treatments relationships and performance evaluation of experiment results.