Forecast of coronary heart disease using data mining classification technique

A Common heart disease is nothing but a cardiovascular disease or coronary heart disease. Predicting the presence of heart disease in prior can be of more use in saving patient’s life. Data mining, a modern technique has provided an automatic way of analyzing data using standard classification method. Classification techniques are the task of generalizing known structure to apply to new large set of data. The work incorporates the classes of heart diseases like myocardial infarction, stroke, congenital heart disease utilizing classification algorithms like support vector machine (SVM) and ensemble methods in particular stacking approach so as to see which combination of algorithms would yield high accuracy compared to other classification algorithms. This paper introduces a prediction model to forecast the presence of heart diseases considering large dataset with missing data for few attributes and makes use of data mining software called Waikato Environment for Knowledge Analysis (WEKA) which includes all techniques of classification.