Data Mining Techniques For Heart Disease Prediction

Data mining is the process of extracting knowledge from hidden information.Today, heart disease is leading cause of death.There exists several data mining techniques to help in diagnosis of heart disease. This paper shows the comparison study of different data mining Algorithms such as K-Means Clustering with Decision Tree, WAC (Weighted Associative Classifier) with Apriori algorithm and Naive Bayes. Different attributes such as age, sex, blood pressure and blood sugar are used in these algorithms to predict the chance of getting a heart disease. The comparison study indicate that K-means clustering with decision tree offers high accuracy.