Data mining techniques have been applied magnificently in many fields including business, science, the Web, bioinformatics, and on different types of data such as textual, visual, spatial and real-time and sensor data. Medical data is still information rich but knowledge poor. There is a lack of effective analysis tools to discover the hidden relationships and trends in medical data obtained from clinical records. This paper reviews the state-of-the-art research on heart disease diagnosis and prediction. DNFS stands for Decision tree based Neural Fuzzy System. Specifically here present an overview of the current research being carried out using the data mining techniques to enhance heart disease diagnosis and prediction including decision trees, Naive Bayes classifiers, K-nearest neighbour classification (KNN), support vector machine (SVM), and artificial neural networks techniques. Results show that SVM and neural networks perform positively high to predict the presence of different types heart diseases. Still the performance of data mining techniques to detect heart diseases is not encouraging.
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