Abstract: Now-a-days, many diseases are reducing the life time of the human. One of the major diseases is cardiovascular disease (CVD). It has become very common perhaps due to increasing busy lifestyles. The issue of health care assumes prime importance for the society and is a significant indicator of social development. Health is therefore best understood as the indispensable basis for defining a personís sense of well-being. Data mining is the computer based process of analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict future trends, allowing business to make proactive, knowledge-driven decisions. The delivery of health care services thus assumes greater proportion, and in this context the role played by information and communication technology has certainly a greater contribution for its effective delivery mechanism. Data mining tools can answer business questions that traditionally taken much time consuming to resolve. The huge amounts of data generated for prediction of CVD are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the predict on of the disease with more accuracy. In this paper we carry different experiments in which one or more algorithms of data mining used for the prediction of CVD. Result from using neural networks is nearly 100%. So that the prediction by using data mining algorithm given efficient results. Applying data mining techniques to CVD treatment data can provide as reliable performance as that achieved in diagnosing CVD.
Keywords: Cardiovascular disease (CVD), Data mining, Genetic algorithm, EM based cluster, Classification, CVD.