Medical science industry has huge amount of data, but unfortunately most of this data is not mined to find out hidden information in data. Advanced data mining techniques can be used to discover hidden pattern in data. Models developed from these techniques will be useful for medical practitioners to take effective decision. In this review paper data mining classification techniques RIPPER classifier, Decision Tree, Artificial neural networks (ANNs), and Support Vector Machine (SVM) are reviewed. In our research work we will compare these techniques through lift chart, error rate and will determine sensitivity, specificity, and accuracy of these data mining techniques.

1. Y. Xing, J. Wang, Z. Zhao, and Y. Gao, "Combination data mining models with new medical data to predict outcome of coronary heart disease", in Proc of International Conference on Convergence Information Technology, 2007 p. 868 – 872.
2. A. Khemphila and V. Boonjing, "Comparing performance of logistic regression, decision trees and neural networks for classifying heart disease patients", in Proc of International Conference on Computer Information System and Industrial Management Applications, 2010, p. 193 – 198.
3. R. Detrano, W. Steinbrunn, M. Pfisterer, J. Schmid, and S. Sandhu, "International application of a new probability algorithm for the diagnosis of coronary artery disease", American Journal of Cardiology, Vol. 64, pp. 304-310, 1987.
4. Z. Yao, L. Lei, and J. Yin, "R-C4. 5 Decision tree model and its applications to health care dataset", in Proc of International Conference on Services Systems and Services Management, 2005, p. 1099-1103.
5. R. Das and S. Abdulkadir, "Effective diagnosis of heart disease through neural networks ensembles", Elsevier, 2008.
6. I. Colombet, A. Ruelland, G. Chatellier, and F. Gueyffier, "Models to predict cardiovascular risk: comparison of CART, multilayer perceptron and logistic regression", in Proc of AMIA Symp, 2000, p. 156-160.
7. E. Avci and I. Turkoglu, "An intelligent diagnosis system based on principle component analysis and ANFIS for the heart valve diseases", Journal of Expert Systems with Application, Vol. 2, pp. 2873-2878, 2009.
8. I. Kurt, M. Ture, and A. Kurum, "Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease", Journal of Expert Systems with Application, Vol. 3, pp. 366-374, 2008.
9. J. Gennari, "Models of incremental concept formation", Journal of Artificial Intelligence, Vol. 1, pp. 11-61, 1989.
10. W. Cohen, "Fast effective rule induction", in Proc of International Conference on machine Learning, 1995, p. 1-10.
11. M. Chau, D. Shin, "A Comparative Study of Medical Data Classification Methods Based on Decision Tree and Bagging Algorithms", in Proc of IEEE International Conference on Dependable, Autonomic and Secure Computing, 2009, p. 183-187.
12. S. Patil, Y. Kumaraswamy, "Intelligent and effective Heart Attack prediction system using data mining and artificial neural networks", European Journal of Scientific Research, Vol. 31, pp. 642- 656, 2009.
13. J. Han and M. Kamber, Data Mining Concepts and Techniques, 2nd Edition, Morgan Kaufmann, San Francisco, 2006.
14. S. Palaniappan and R. Awang, "Intelligent Heart Disease Prediction System Using Data Mining Techniques", in Proc of IEEE/ACS International Conference on Computer Systems and Applications, 2008, p. 108-115.

Heart Disease Data Mining Techniques Ripper Decision Tree Artificial Neural Networks And Support Vector Machine.