A Class Based Approach for Medical Classification of Chest Pain

A Class Based Approach for Medical Classification of Chest Pain

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2012 by IJETT Journal
Volume-3 Issue-2                          
Year of Publication : 2012
Authors :  K.RuthRamya, K.Anusha, K.Chanti, V.Sri Vidya, P.Praveen Kumar

Citation 

K.RuthRamya,K.Anusha, K.Chanti,V.Sri Vidya, P.Praveen Kumar . " A Class Based Approach for Medical Classification of Chest Pain". International Journal of Engineering Trends and Technology (IJETT). V3(2):89-93 Mar-Apr 2012. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

This paper focuses on class based data mining algorithm and their use in medical applications. 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 emerg ency departments. This study concentrates on building a hybrid methodology, based on using A New Class Based Associative Classification Algorithm which is an advanced and efficient approach than all other association and classification Data Mining algorit hms . Applying the association rule into classification can improve the accuracy and obtain some valuable rules and information that cannot be captured by other classification approaches. The class label is taken good advantage in the rule mining step so as to cut down the searching space. The proposed algorithm also synchronize the rule generation and classifier building phases, shrinking the rule mining space when building the classifier to help speed up the rule generation.

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] I. Kononenko, “Machine learning for medical diagnosis: history, state of the art and perspective,” Artificial Intelligence in Medicine , vol. 23, pp. 89 - 109, 2001.
[3] G. Masuda, N. Sakamoto, and R. Yamamoto, “A framework for dynamic evidence based medicine us ing data mining,” In Proc. 15th IEEE Symposium on Computer - Based Medical Systems , IEEE press, 2002, pp. 117 - 122.
[4] M. J. Zaki, “Mining non - redundant association rules,” Data Mining and Knowledge Discovery , vol. 9, pp. 223 - 248, 2004.
[5] I. H. Witten and E. Frank , Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.). Morgan Kaufmann, CA: San Francisco, 2005.
[6] K. H. Butler and S. A. Swencki, “Chest pain: a clinical assessment,” Radiologic Clinics of North America , vol. 44, pp. 165 - 179, 2006.
[7] 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.
[8] 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.
[9] C. Duguary, and F. Chet ouane, “Modeling and improving emergency department systems using discrete event simulation,” Simulation , vol. 83, pp. 311 - 320, 2007.
[10] H. Ren, “Clinical diagnosis of chest pain,” Chinese Journal for Clinicians , vol. 36, 2008.
[11] 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.
[12] Y. P. Yun, “Application and research of data mining based on C4.5 Algorithm,” Ma ster thesis , Haerbin University of Science and Technology, 2008.
[13] 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.
[14] 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.
[15] 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.
[16] 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.

Keywords
Data mining, medical decisions, medical domain knowledge, dataset, pruning, rule mining, chest pain.