The health care environment is found to be rich in information, but poor in extracting knowledge from the information. This is because of the lack of effective analysis tool to discover hidden relationships and trends in them. By applying the data mining techniques, valuable knowledge can be extracted from the health care system. Heart disease is a group of condition affecting the structure and functions of the heart and has many root causes. Heart disease is the leading cause of death in the world over past ten years. Researches have been made with many hybrid techniques for diagnosing heart disease. This paper deals with an overall review of the application of data mining in heart disease prediction.

Cardio Vascular Disease, Data Mining, Feature Selection, Classification, Association Rule Mining, Clustering

[1] Nahar, Jasmine, et al, “Association rule mining to detect factors which contribute to heart disease in males and females”, Expert Systems with Applications, Vol. 40 Issue. 4, pp. 1086-1093, 2013.
[2] Vijiyarani, S., and S. Sudha, “An efficient classification tree technique for heart disease prediction”, International Conference on Research Trends in Computer Technologies (ICRTCT-2013) Proceedings published in International Journal of Computer Applications (IJCA)(0975–8887). Vol. 201, 2013.
[3] Gayathri, P., and N. Jaisankar, “Comprehensive study of heart disease diagnosis using data mining and soft computing techniques”, 2013.
[4] Shouman, Mai, Tim Turner, and Rob Stocker, “Integrating clustering with different data mining techniques in the diagnosis of heart disease”, J. Comput. Sci. Eng, Vol. 20 Issue.1, 2013.
[5] Amato, Filippo, et al, “Artificial neural networks in medical diagnosis”, pp. 47-58, 2013.
[6] Persi Pamela, I., and P. Gayathri, “A fuzzy optimization technique for the prediction of coronary heart disease using decision tree”, 2013.
[7] Chaurasia, Vikas, and Saurabh Pal, “Data mining approach to detect heart diseases”, 2014.
[8] Thenmozhi, K., and P. Deepika, “Heart disease prediction using classification with different decision tree techniques”, International Journal of Engineering Research and General Science, Vol. 2, Issue. 6, pp. 6-11, 2014.
[9] Kim, Jae-Kwon, et al, “Adaptive mining prediction model for content recommendation to coronary heart disease patients”, Cluster computing, Vol. 17, Issue. 3, pp. 881-891, 2014.
[10] Seera, Manjeevan, and Chee Peng Lim, “A hybrid intelligent system for medical data classification”, Expert Systems with Applications, Vol. 41, Issue. 5, pp. 2239-2249, 2014.
[11] Bashir, Saba, Usman Qamar, and M. Younus Javed, “An ensemble based decision support framework for intelligent heart disease diagnosis”, Information Society (i-Society), 2014 International Conference on. IEEE, 2014.
[12] Shabana, ASMI P., and S. Justin Samuel, “An analysis and accuracy prediction of heart disease with association rule and other data mining techniques”, Journal of Theoretical and Applied Information Technology, Vol. 79, Issue. 2, pp. 254-60, 2015.
[13] Aljaaf, A. J., et al, “Predicting the likelihood of heart failure with a multi level risk assessment using decision tree”, Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2015 Third International Conference on. IEEE, 2015.
[14] Bashir, Saba, Usman Qamar, and Farhan Hassan Khan, “BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting”, Australasian physical & engineering sciences in medicine, Vol. 38, Issue. 2, pp. 305-323, 2015.
[15] Kim, Jaekwon, Jongsik Lee, and Youngho Lee, “Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree”, Healthcare informatics research, Volume. 21, Issue. 3, pp. 167-174, 2015.