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Empirical Evaluation of Ensemble Approach for Diagnostic Knowledge Management

진단지식관리를 위한 앙상블 기법의 실증적 평가

  • Ha, Sung-Ho (School of Business, Kyungpook National University) ;
  • Zhang, Zhen-Yu (School of Business, Kyungpook National University)
  • 하성호 (경북대학교 경영학부) ;
  • 장전위 (경북대학교 일반대학원 경영학부)
  • Received : 2011.08.05
  • Accepted : 2011.09.04
  • Published : 2011.09.30

Abstract

지난 수십 년 간 연구자들은 효과적인 진료지원시스템을 개발하기 위해 다양한 도구와 방법론들을 제안하였고 지금도 새로운 방법론과 도구들을 계속적으로 개발하고 있다. 그 중에서 흉통으로 응급실에 내원한 노인환자에 대한 정확한 진단은 중요한 이슈 중의 하나였다. 따라서 많은 연구자들이 의사의 진단 능력을 향상시키기 위한 지능적인 의료의사결정과 시스템 개발에 투신하고 있지만 전통적인 의료시스템에 따른 대부분의 진료의사결정이 단일 분류기(classifier)에 기반하고 있어 만족스런 성능을 보여주지 못하고 있는 것이 현실이다. 따라서 이 논문은 앙상블 전략을 활용하여 의사들이 노인환자들의 흉통을 더 정확하고 빠르게 진단하는데 있어 도움을 줄 수 있게 하였다. 의사결정나무, 인공신경망, SVM 모델을 결합한 앙상블 기법을 실제 응급실에서 수집한 응급실 자료에 적용하였고, 그 결과 단일 분류기를 사용하는 것에 비해 월등히 향상된 진단 성과를 보이는 것을 관찰 할 수 있었다.

Keywords

References

  1. Abdullah, U., Ahmad, J., and Ahmed, A., "Analysis of Effectiveness of Apriori Algorithm in Medical Billing Data Mining," Proceedings of 4th International Conference on Emerging Technologies, 2008, pp.327-331.
  2. Bassan, R., Scofano, M., Gamarski, R., Dohmann, H.F., Pimenta, L., Volschan, A., Araujo, M., Clare, C., Fabricio, M., Sanmartin, C.H., Mohallem, K., Gaspar, S., and Macaciel, R., "Chest Pain in the Emergency Room: Importance of a Systematic Approach," Arquivos Brasileiros de Cardiologia, Vol,74, No.1, 2000, pp.13-29.
  3. Ceglowski, R., Churilov, L., and Wasserthiel, J., "Combining Data Mining and Discrete Event Simulation for a Value-Added View of a Hospital Emergency Department," Journal of the Operational Research Society, Vol.58, 2007, pp.246-254. https://doi.org/10.1057/palgrave.jors.2602270
  4. Cios, K. J., and Moore, G.W., "Uniqueness of Medical Data Mining," Artificial Intelligence in Medicine, Vol.26, No.1, 2002, pp.1-24. https://doi.org/10.1016/S0933-3657(02)00049-0
  5. Conforti, D., and Guido, R., "Kernel-Based Support Vector Machine Classifiers for Early Detection of Myocardial Infarction," Optimization Methods and Software, Vol.20, No.2-3, 2005, pp.401-413. https://doi.org/10.1080/10556780512331318164
  6. Conroy, R.M., Pyorala, K., Fitzgerald, A. P., Sans, S., Menotti, A., De Backer, G., De Bacquer, D., Ducimetiere, P., Jousilahti, P., and Keil, U., "Estimation of Ten-Year Risk of Fatal Cardiovascular Disease in Europe: the SCORE Project," European Heart Journal, Vol.24, No.11, 2003, pp.987-1003. https://doi.org/10.1016/S0195-668X(03)00114-3
  7. Duguay, C., and Chetouane, F., "Modeling and Improving Emergency Department Systems Using Discrete Event Simulation," Simulation, Vol.83, No.4, 2007, pp.311-320. https://doi.org/10.1177/0037549707083111
  8. Ellenius, J., and Groth, T., "Transferability of Neural Network-Based Decision Support Algorithms for Early Assessment of Chest-Pain Patients," International Journal of Medical Informatics, Vol.60, No.1, 2000, pp.1-20. https://doi.org/10.1016/S1386-5056(00)00064-2
  9. Erhardt, L., Herlitz, J., and Bossaert, L., "Task Force on the Management of Chest Pain," European Heart Journal, Vol.23, 2002, pp.1153-1176. https://doi.org/10.1053/euhj.2002.3194
  10. Fayyad, U.M., "Data Mining and Knowledge Discovery in Databases: Implications for Scientific Databases," Proceedings of Ninth International Conference on Scientific and Statistical Database Management, Vol.2, 2002, pp.11-13.
  11. Fromm, R.E., Gibbs, L.R., McCallum, W.G., Niziol, C., Babcock, J.C., and Gueler, A.C., "Critical Care in the Emergency Department: A Time-Based Study," Critical Care Medicine, Vol.21, No.7, 1993, pp.970-976. https://doi.org/10.1097/00003246-199307000-00009
  12. Guven, A., and Kara, S., "Classification of Electro-Oculogram Signals Using Rtificial Neural Network," Expert Systems with Applications, Vol.31, No.1, 2006, pp.199-205. https://doi.org/10.1016/j.eswa.2005.09.017
  13. Kannel, W.B., and Vasan, R.S., "Is Age Really a Non-Modifiable Cardiovascular Risk Factor," American Journal of Cardiology, Vol.104, No.9, 2009, pp.1307-1310. https://doi.org/10.1016/j.amjcard.2009.06.051
  14. Kantardzic, M.M., and Zurada, J., Next Generation of Data-Mining Applications, John Wiley & Sons, Hoboken, 2005.
  15. Kenneth, H.B., and Sharon, A.S., "Chest Pain: A Clinical Assessment," Radiologic Clinics of North America, Vol.44, No.2, 2006, pp.165-179. https://doi.org/10.1016/j.rcl.2005.11.002
  16. Khan, F.S., Anwer, R.M., Torgersson, O., and Falkman, G., "Data Mining in Oral Medicine Using Decision Trees," World Academy of Science, Engineering and Technology, Vol.37, 2008, pp.225-230.
  17. Kononenko, I., "Machine Learning for Medical Dagnosis: History, State of the Art and Perspective," Artificial Intelligence in Medicine, Vol.23, No.1, 2001, pp.89-109. https://doi.org/10.1016/S0933-3657(01)00077-X
  18. Lin, W.T., Wang, S.T., Chiang, T.C., Shi, Y.X., Chen, W.Y., and Chen, H.M., "Abnormal Diagnosis of Emergency Department Triage Explored with Data Mining Technology: An Emergency Department at a Medical Center in Taiwan Taken as an Example," Expert Systems with Applications, Vol.37, No.4, 2010, pp.2733-2741. https://doi.org/10.1016/j.eswa.2009.08.006
  19. Majumder, S.K., Ghosh, N., and Gupta, P.K., "Support Vector Machine for Optical Diagnosis of Cancer," Journal of Biomedical Optics, Vol. 10, No.2, 2005, pp.24-34.
  20. Martinez-Selles, M., Bueno, H., Sacristan, A., Estevez, A., Ortiz, J., Gallego, L., and Fernandez-Aviles, F., "Chest Pain in the Emergency Department: Incidence, Clinical Characteristics and Risk Stratification," Revista Espanola de Cardiologia, Vol.61, No.9, 2008, pp. 953-959. https://doi.org/10.1016/S1885-5857(08)60256-X
  21. Mitchell, T.M., "Machine Learning and Data Mining," Communications of the ACM, Vol.42, No.11, 1999, pp.30-36.
  22. Qiao, D.D., "Clinical Diagnosis of Life Threatening Chest Diseases Based on 156 Cases," Journal of Chinese Modern Medicine, Vol.32, No.4, 2009, pp.11-13.
  23. Quinlan, J.R., C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, 1993.
  24. Ren, H., "Clinical Diagnosis of Chest Pain," Chinese Journal for Clinicians, Vol.36, No.1, 2008, pp.5-7.
  25. Ridker, P.M., Hennekens, C.H., Buring, J.E., and Rifai, N., "C-Reactive Protein and Other Markers of Inflammation in the Prediction of Cardiovascular Disease in Women," New England Journal of Medicine, Vol.342, 2000, pp.836-843. https://doi.org/10.1056/NEJM200003233421202
  26. Rossille, D., Cuggia, M., Arnault, A., Bouget, J., and Le Beux, P., "Managing an Emergency Department by Analyzing HIS Medical Data: A Focus on Elderly Patient Clinical Pathways," Health Care Management Science, Vol.11, No.2, 2008, pp.139-146. https://doi.org/10.1007/s10729-008-9059-6
  27. Shmueli, G., Patel, N.R., and Bruce, P.C., Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, John Wiley & Sons, Hoboken, 2010.
  28. Statistics Korea, http://www.kostat.go.kr/, 2010.
  29. Tan, Y., Yin, G.F., Li, G.B., and Chen, J.Y., "Mining Compatibility Rules from Irregular Chinese Traditional Medicine Database by Apriori Algorithm," Journal of Southwest JiaoTong University, Vol.15, No.4, 2009, pp.288-293.
  30. Yun, Y.P., "Application and Research of Data Mining based on C4.5 Algorithm," Harbin University of Science and Technology, Vol.10, No.8, 2008, pp.311-315.
  31. Zaki, M.J., "Mining Non-Redundant Association rules," Data Mining and Knowledge Discovery, Vol.9, No.3, 2004, pp.223-248. https://doi.org/10.1023/B:DAMI.0000040429.96086.c7

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