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Performance Evaluation of Decision Tree Classifiers on Medical Datasets
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 26 - Number 4 |
| Year of Publication: 2011 |
| Authors: D.Lavanya, Dr. K.Usha Rani |
10.5120/3095-4247
|
D.Lavanya, Dr. K.Usha Rani . Performance Evaluation of Decision Tree Classifiers on Medical Datasets. International Journal of Computer Applications. 26, 4 ( July 2011), 1-4. DOI=10.5120/3095-4247
Abstract
In data mining, classification is one of the significant techniques with applications in fraud detection, Artificial intelligence, Medical Diagnosis and many other fields. Classification of objects based on their features into pre-defined categories is a widely studied problem. Decision trees are very much useful to diagnose a patient problem by the physicians. Decision tree classifiers are used extensively for diagnosis of breast tumour in ultrasonic images, ovarian cancer and heart sound diagnosis. In this paper, performance of decision tree induction classifiers on various medical data sets in terms of accuracy and time complexity are analysed.
References
- J. Han and M. Kamber, “Data Mining; Concepts and Techniques, Morgan Kaufmann Publishers”, 2000.
- T. Mitchell, “Machine Learning”, McGraw Hill, 1997.
- R. Brachman, T. Khabaza, W.Kloesgan, G.Piatetsky-Shapiro and E. Simoudis, “Mining Business Databases”, Comm. ACM, Vol. 39, no. 11, pp. 42-48, 1996.
- U.M. Fayyad, G. Piatetsky-Shapiro and P. Smyth, “From Data Mining to knowledge Discovery in Databases”, AI Magazine, vol 17, pp. 37-54, 1996.
- Antonia Vlahou, John O. Schorge, Betsy W.Gregory and Robert L. Coleman, “Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data”, Journal of Biomedicine and Biotechnology • 2003:5 (2003) 308–314.
- Kuowj, Chang RF,Chen DR and Lee CC,” Data Mining with decision trees for diagnosis of breast tumor in medical ultrasonic images” ,March 2001.
- H. Ren, “Clinical diagnosis of chest pain,” Chinese Journal for Clinicians, vol. 36, 2008.
- My Chau Tu, Dongil Shin, Dongkyoo Shin, “A Comparative Study of Medical Data Classification Methods Based on Decision Tree and Bagging Algorithms”, DASC '09 Proceedings of the 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE Computer Society Washington, DC, USA ©2009.
- Sung Ho Ha and Seong Hyeon Joo, “A Hybrid Data Mining Method for the Medical Classification of Chest Pain”, World Academy of Science, Engineering and Technology 70 2010.
- Matthew N.Anyanwu, Sajjan G.Shiva, “Comparative Analysis of Serial Decision Tree Classification Algorithms”, International Journal of Computer Science and Security, volume 3.
- G Stasis, A.C. Loukis, E.N. Pavlopoulos, S.A. Koutsouris, D. “Using decision tree algorithms as a basis for a heart sound diagnosis decision support system”, Information Technology Applications in Biomedicine, 2003. 4th International IEEE EMBS Special Topic Conference, April 2003.
- Quinlan, J.R, “Induction of decision trees”. Journal of Machine Learning 1(1986) 81-106.
- J.R.Quinlan,”c4.5: Programs for Machine Learning”, Morgan Kaufmann Publishers, Inc, 1992.
- Breiman, Friedman, Olshen, and Stone. “Classification and Regression Trees”, Wadsworth, 1984., Mezzovico, Switzerland.
- UC Irvine Machine Learning Repository, www.ics.uci.edu/~mlearn/MLRepository.html
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