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

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.

Authors:

• Sung Ho Ha
• Seong Hyeon Joo

Keywords:

• Data mining
• medical decisions
• medical domainknowledge
• chest pain.

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.