Review on Heart Disease Prediction System using Data Mining Techniques

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. Data mining tools can answer business questions that traditionally taken much time consuming to resolve. The huge amounts of data generated for prediction of heart disease 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 prediction of the disease with more accuracy. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. Result from using neural networks is nearly 100% in one paper (10) and in (6). So that the prediction by using data mining algorithm given efficient results. Applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart disease. Cholesterol: - abnormal levels of lipids (fats) in the blood are risk factor of heart diseases. Cholesterol is a soft, waxy substance found among the lipids in the bloodstream and in all the body's cells. High level of triglyceride (most common type of fat in body) combined with high levels of LDL (low density lipoprotein) cholesterol speed up atherosclerosis increasing the risk of heart diseases. High blood pressure: - High blood pressure also known as HBP or hypertension is a widely misunderstood medical condition. High blood pressure increase the risk of the walls of our blood vessels walls becoming overstretched and injured. Also increase the risk of having heart attack or stroke and of developing heart failure, kidney failure and peripheral vascular disease.

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