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A Hybrid Data Mining Method for the Medical Classification of Chest Pain

Authors: Sung Ho Ha, Seong Hyeon Joo


Data mining techniques have been used in medical research for many years and have been known to be effective. In order to solve such problems as long-waiting time, congestion, and delayed patient care, faced by emergency departments, this study concentrates on building a hybrid methodology, combining data mining techniques such as association rules and classification trees. The methodology is applied to real-world emergency data collected from a hospital and is evaluated by comparing with other techniques. The methodology is expected to help physicians to make a faster and more accurate classification of chest pain diseases.

Keywords: Data Mining, Chest pain, medical decisions, medical domainknowledge

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[1] R. E. Fromm, L. R. Gibbs, W. G. McCallum, C. Niziol, J. C. Babcock, A. C. Gueler, and R. L. Levine, "Critical care in the emergency department: a time-based study", Crit. Care Med., vol. 21, pp. 970-976, 1993.
[2] B. Riccardo and Z. Blaz, "Predictive data mining in clinical medicine: Current issues and guidelines," International Journal of Medical Informatics, vol. 77, pp. 81-97, 2008.
[3] G. Masuda, N. Sakamoto, and R. Yamamoto, "A framework for dynamic evidence based medicine using data mining," In Proc. 15th IEEE Symposium on Computer-Based Medical Systems, IEEE press, 2002, pp. 117-122.
[4] I. Kononenko, "Machine learning for medical diagnosis: history, state of the art and perspective," Artificial Intelligence in Medicine, vol. 23, pp. 89-109, 2001.
[5] F. S. Khan, R. M. Anwer, O. Torgersson, and G. Falkman, "Data mining in oral medicine using decision trees," International Journal of Biological and Medical Sciences, vol. 4, pp. 156-161, 2009.
[6] Y. P. Yun, "Application and research of data mining based on C4.5 Algorithm," Master thesis, Haerbin University of Science and Technology, 2008.
[7] U. Abdullah, J. Ahmad, A. Ahmed, "Analysis of effectiveness of apriori algorithm in medical billing data mining," In Proc. 4th International Conference on Emerging Technologies, IEEE press, 2008, pp. 327-331.
[8] Y. Tan, G. F. Yin, G. B. Li, and J. Y. Chen, "Mining compatibility rules from irregular Chinese traditional medicine database by Apriori algorithm," Journal of Southwest JiaoTong University, vol. 15, 2007.
[9] R. Ceglowski, L. Churilov, and J. Wasserthiel, "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, pp. 246-254, 2007.
[10] R. Delphine, M. Cuggia, A. Arnault, J. Bouget, and P. L. Beux, "Managing an emergency department by analyzing HIS medical data: a focus on elderly patient clinical pathways," Health Care Management Science, vol. 11, pp. 139-146, 2008.
[11] W. T. Lin, S. T. Wang, T. C. Chiang, Y. X. Shi, W. Y. Chen, and H. M. Chen, "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, pp. 2733-2741, 2010.
[12] C. Duguary, and F. Chetouane, "Modeling and improving emergency department systems using discrete event simulation," Simulation, vol. 83, pp. 311-320, 2007.
[13] M. J. Zaki, "Mining non-redundant association rules," Data Mining and Knowledge Discovery, vol. 9, pp. 223-248, 2004.
[14] I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.). Morgan Kaufmann, CA: San Francisco, 2005.
[15] K. H. Butler and S. A. Swencki, "Chest pain: a clinical assessment," Radiologic Clinics of North America, vol. 44, pp. 165-179, 2006.
[16] H. Ren, "Clinical diagnosis of chest pain," Chinese Journal for Clinicians, vol. 36, 2008.