DOI: 10.4018/IJBDAH.2018070101 International Journal of Big Data and Analytics in Healthcare Volume 3 • Issue 2 • July-December 2018 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 1 Prediction of Heart Diseases Using Data Mining Techniques: Application on Framingham Heart Study Nancy Masih, Chitkara University, Khanna, India Sachin Ahuja, Chitkara University, Punjab, Chandigarh, India ABSTRACT Health care organizations accumulate large amount of healthcare data, but it is not ‘extracted’ to draw hidden patterns which can prove efficient for the decision making process. Data mining techniques can be used to gain insights by discovering hidden patterns which remain undetected manually. Data analytics proves to be useful in detection and identification of the diseases. A complete analysis has been conducted on the FHS (Framingham Heart Study) using various data analytic techniques viz. Decision tree, Naïve Bayes, Support vector machine (SVM) and Artificial neural network (ANN) and the results were ranked according to the accuracy. ANN produce better results than other classification algorithms. The output helps to find out the prominent features that cause heart disease and also identifies the most common features that must be analyzed for prediction of deaths due to heart disease. Despite various studies carried out on heart diseases, the main focus of this study is prediction of heart disease on the dataset of FHS by using various classification algorithms to achieve high accuracy. KeywoRDS Artificial Neural Network, Decision Tree, Framingham Heart Study, Heart Disease, Naïve Bayes, Prediction, Support Vector Machine 1. INTRoDUCTIoN Coronary heart disease (CHD) is convicted as the leading reason for mortality rate worldwide according to WHO (World Health Organization). It is observed that CHD is the cause considered for 17.7 million deaths every year and more than twenty-four million ratio of people anticipated passing from cardiovascular sickness by the year of 2030 (Kinge & Gaikwad, 2018) CHD dominates other diseases with its severe effects on a person’s wellbeing worldwide (Wilson et al., 1998). In earlier times, the risk of the respective disease used to be analyzed by personal experience of doctors and patients and which was highly vulnerable to various errors and lack of hidden patterns were also observed (Palaniappan & Awang, 2008). Manual decisions are based upon the knowledge and experience of doctors’ which cannot be always accurate. Therefore, it is vulnerable to various errors and hence diminishes quality of treatment given to patients. The errors of traditional methods give rise to the various new techniques