Published Paper Details:

Cardiovascular disease or heart disease is the world�s largest cause of weariness and death. Predicting cardiac disease before hand is most important in the analysis of clinical data. A large amount of data is present in health care industry ,that data can be converted into information , with the help of that information predictions can be made. Several researchers have applied data mining technique to predict heart disease. It is very important to select appropriate set of attributes and data mining algorithm to enhance predictive accuracy. This research is done to select appropriate attributes and data mining algorithms that can enhance predictive accuracy of heart disease. Different sets of attributes and classification techniques were used to develop prediction model. K-NN, Logistic Regression, Support Vector Machine,Decision Tree, Na�ve Bayes, Vote(Hybrid technique with na�ve bayes and logistic regression) and Stacking(A hybrid technique with na�ve bayes, logistic regression as base learners and support vector machine as model learner).This study shows that the cardiac disease prediction model was developed by using appropriate attributes and best results of data mining technique(stacking) got an accuracy of 85.71% in predicting cardiac disease.