Coronary Heart Disease (CHD) is a common form of disease affecting the heart and an important cause for premature death. From the point of view of medical sciences, data mining is involved in discovering various sorts of metabolic syndromes. Classification techniques in data mining play a significant role in prediction and data exploration. Classification technique such as Decision Trees has been used in predicting the accuracy and events related to CHD. In this paper, a Data mining model has been developed using Random Forest classifier to improve the prediction accuracy and to investigate various events related to CHD. This model can help the medical practitioners for predicting CHD with its various events and how it might be related with different segments of the population. The events investigated are Angina, Acute Myocardial Infarction (AMI), Percutaneous Coronary Intervention (PCI), and Coronary Artery Bypass Graft surgery (CABG). Experimental results have shown that classification using Random Forest Classification algorithm can be successfully used in predicting the events and risk factors related to CHD.

1. British Heart Foundation. (2008, mar. 8). European Cardio-vascular Disease Statistics.
2. Z. J Zhang and S. H Wang 'Synopsis of Prescriptions of the Golden Chamber' Beijing: People's Medical Publishing House, pp. 27–29, 2007.
3. C. L. Tsien, H. S. F. Fraser, W. J. Long and R. L. Kennedy "Using classification trees and logistic regression methods to diagnose myocardial infarction" in Proc. 9th World Congr. , Inf. , vol. 52, pp. 483-497, 2001.
4. T. D. Rea, S. R. Heckbert, R. C. Kaplan, N. L. Smith, R. N. Lemaitre, and B. M. Psaty, "Smoking status and risk for recurrent coronary events after myocardial infarction" Ann. Int. Med. , vol. 137, pp. 494-500, 2002.
5. Z. Wang and W. E. Hoy "Is the Framingham coronary heart disease absolute risk function applicable to Aboriginal people?" Med. J. Australia, Vol. 182, No. 2, pp. 66-69, 2005.
6. M. Karaolis, J. A. Moutiris, L. Pattichs "Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees", IEEE Transactions on IT in Biomedicine, vol. 14, No. 3, 2010.
7. J. Kunc, S. Drinovec, Rucigaj, and A. Mrhar, "Simulation analysis of coronary heart disease, congestive heart failure and end-stage renal disease economic burden. " Mathematics and computers in simulation, 2010.
8. K. Srinivas, G. Raghavendra, Rao, and A. Govardhan, "Analysis of Coronary Heart Disease and Prediction of Heart Attack in Coal Mining Regions Using Data Mining Techniques", 5th IntConf on Computer Science & Education Hefei, China, pp. 1344-1349, 2010.
9. J. Soni, U. Ansari, and D. Sharmaa "Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction" Int. , Journal of Computer Applications vol. 17, No. 8, 2011.
10. A. Rafiah and P. Sellappan "Intelligent Heart Disease Prediction System Using Data Mining Techniques", 2008.
11. I. H. Karlberg, and S. L. Elo "Validity and utilization of epidemiological data: A study of Ischaemic heart disease and coronary risk factors in a local population", 2009.
12. L. Breiman and A. Cutler " www. stat. berkeley. edu".
13. A. Reena and S. G. Rajkumar, "Diagnosis of heart disease using Data mining algorithm", Global Journal of Computer science and Technology, Vol. 10, No. 10, 2010.

Coronary Heart Disease Decision Trees Random Forest