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An Overview of Data Mining Techniques Applied for Heart Disease Diagnosis and Prediction

Salha M. Alzahani, Afnan Althopity, Ashwag Alghamdi, Boushra Alshehri, and Suheer Aljuaid
Dept. of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
Abstract—Data mining techniques have been applied magnificently in many fields including business, science, the Web, cheminformatics, bioinformatics, and on different types of data such as textual, visual, spatial, 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. Specifically in this paper, we 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 coronary heart diseases (CHD). Decision trees after features reduction is the best recommended classifier to diagnose cardiovascular disease (CVD). Still the performance of data mining techniques to detect coronary arteries diseases (CAD) is not encouraging (between 60%-75%) and further improvements should be pursued.

Index Terms—heart disease, data mining, decision tree, naive bayes, K-nearest neighbor, support vector machine

Cite: Salha M. Alzahani, Afnan Althopity, Ashwag Alghamdi, Boushra Alshehri, and Suheer Aljuaid, "An Overview of Data Mining Techniques Applied for Heart Disease Diagnosis and Prediction," Lecture Notes on Information Theory, Vol. 2, No. 4, pp. 310-315, December 2014. doi: 10.12720/lnit.2.4.310-315
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