ISSN: 1007-1172

Impact Factor: 6.2


Journal of Shanghai Jiaotong University

International Peer Reviewed | Open Access | A Monthly Publishing Journal



M.Malathi, Ph.D. ResearchScholar
PG and Research Department of Computer Science L.R.G.GovernmentArtsCollegeforWomen Tirupur, Tamil Nadu, India.

Dr.T. Jayalakshmi, Assistant Professor,
PG and Research Department of Computer Science Government Arts College, Coimbatore, Tamil Nadu, India.


In the digital era, there are abundant data created and are stored in repositories. Health sector is no exception from it. But these data are not analyzed with proper techniques in order to extract the hidden information from it. In recent days, Data mining has wide wings in its application. Medical field is one of the application areas of data mining techniques. Although there are number of diseases that affect human, not all are life threatening in nature. Heart disease being one of the serious diseases has to be predicted and treated at the earliest to avoid death rate. Every year millions of people are affected with this disease. More than one third of the world’s population affected by this disease. If not treated on proper time it will lead to deaths in people under 70 years of age. Early diagnosis of cardiovascular disease are challenging task, and computer aided methods has been proposed to overcome the disease. Revealing facts about the disease supports the physician in decision making. It can be achieved by KDD (Knowledge Discovery in Databases) process using data mining techniques. The objective of this study is to carry out empirical analysis on various applications of data mining and machine learning techniques. Some of the techniques used worldwide by the researchers for the diagnosis of heart disease are support vector machine, neural network, naive Bayes, conventional neural network, k-Nearest Neighbor, Decision tree and so on.