DOI QR코드

DOI QR Code

Developing an Intelligent Health Pre-diagnosis System for Korean Traditional Medicine Public User

  • Kim, Kwang Baek (Division of Computer Software Engineering, Silla University)
  • Received : 2017.05.29
  • Accepted : 2017.06.07
  • Published : 2017.06.30

Abstract

Expert systems for health diagnosis are only for medical experts who have deep knowledge in the field but we need a self-checking pre-diagnosis system for preventive public health monitoring. Korea Traditional Medicine is popular in use among Korean public but there exist few available health information systems on the internet. A computerized self-checking diagnosis system is proposed to reduce the social cost by monitoring health status with simple symptom checking procedures especially for Korea Traditional Medicine users. Based on the national reports for disease/symptoms of Korea Traditional Medicine, we build a reliable database and devise an intelligent inference engine using fuzzy c-means clustering. The implemented system gives five most probable diseases a user might have with respect to symptoms given by the user. Inference results are verified by Korea Traditional Medicine doctors as sufficiently accurate and easy to use.

Keywords

References

  1. A. G. Karegowda, A. S. Manjunath, and M. A. Jayaram, "Application of genetic algorithm optimized neural network connection weights for medical diagnosis of Pima Indians diabetes," International Journal on Soft Computing, vol. 2, no. 2, pp. 15-23, 2011. https://doi.org/10.5121/ijsc.2011.2202
  2. M. Fathi-Torbaghan and D. Meyer, "MEDUSA: a fuzzy expert system for medical diagnosis of acute abdominal pain," Methods of Information in Medicine, vol. 33, no. 5, pp. 522-529, 1994. https://doi.org/10.1055/s-0038-1635055
  3. C. D. Hong, "Complementary and alternative medicine in Korea: current status and future prospects," The Journal of Alternative & Complementary Medicine, vol. 7, no. 1, pp. 33-40, 2001.
  4. S. K. Bong, "A study on the preservation and utilization of Dongeuibogam," Journal of Korea Institute of Oriental Medicine, vol. 15, no. 1, pp. 31-42, 2009.
  5. K. B. Kim and J. W. Kim, "Self health diagnosis system with Korean traditional medicine using fuzzy ART and fuzzy inference rules," in Intelligent Information and Database Systems, Lecture Notes in Computer Science, vol. 7198, pp. 326-335, 2012.
  6. M. Kim, H. R. Han, K. B. Kim, and D. N. Duong, "The use of traditional and Western medicine among Korean American Elderly," Journal of Community Health, vol. 27, no. 2, pp. 109- 120, 2006. https://doi.org/10.1023/A:1014509200352
  7. D. Y. Chung, D. K. Baek, S. I. Hwang, S. H. Shin, D. W. Kim, and M. A. Han, "One case of systemic lupus erythematosus treated by integrated therapy of western medicine with oriental differential diagnosis of symptoms and signs," Journal of Korean Traditional Internal Medicine, vol. 23, no. 2, pp. 306-312, 2002.
  8. H. Xu and K. J. Chen, "Integrating traditional medicine with biomedicine towards a patient-centered healthcare system," Chinese Journal of Integrative Medicine, vol. 17, no. 2, pp. 83-84, 2011. https://doi.org/10.1007/s11655-011-0641-2
  9. B. Lim, J. Park, and C. Han, "Attempts to utilize and integrate traditional medicine in North Korea," The Journal of Alternative & Complementary Medicine, vol. 15, no. 3, pp. 217-223, 2009. https://doi.org/10.1089/acm.2008.0294
  10. D. L. Berry, L. J. Trigg, W. B. Lober, B. T. Karras, M. L. Galligan, M. Austin-Seymour, and S. Martin, "Computerized symptom and quality-of-life assessment for patients with cancer. Part I. Development and pilot testing," Oncology Nursing Forum, vol. 31, no. 5, pp. E75-E83, 2004.
  11. K. H. Mullen, D. L. Berry, and B. K. Zierler, "Computerized symptom and quality-of-life assessment for patients with cancer. Part II. Acceptability and usability," Oncology Nursing Forum, vol. 31, no. 5, pp. E84-E89, 2004. https://doi.org/10.1188/04.ONF.E84-E89
  12. Korea Ministry of Health and Welfare [Internet], Available: http://www.mohw.go.kr.
  13. S. Moein, S. A. Monadjemi, and P. A. Moallem, "A novel fuzzyneural based medical diagnosis system," in Proceedings of World Academy of Science, Engineering and Technology, Tokyo, Japan, 2009.
  14. C. Y. Fan, P. C. Chang, J. J. Li, and J. C, Hsieh, "A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification," Applied Soft Computing, vol. 11, no. 1, pp. 632-644, 2011. https://doi.org/10.1016/j.asoc.2009.12.023
  15. K. B. Kim, S. Kim, and G. H. Kim, "Nucleus classification and recognition of uterine cervical pap-smears using FCM clustering algorithm," in Adaptive and Natural Computing Algorithms, Heidelberg: Springer, pp. 290-299, 2007.
  16. B. H. Chung, T. R. Chun, H. C. Kim, and K. B. Kim, "Self health diagnosis system using enhanced FCM algorithm," in Proceedings of KIICE Conference, pp. 143-149, 2006.
  17. Korea National Statistical Office [Internet], Available: http://www.kostat.go.kr.