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

Authors: Sung Ho Ha, Seong Hyeon Joo

Abstract:

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, medical decisions, medical domainknowledge, chest pain.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076134

References:

[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.