International Journal of Electrical and Computer Engineering (IJECE) Vol. 6, No. 4, August 2016, pp. 1800~1810 ISSN: 2088-8708, DOI: 10.11591/ijece.v6i4.9902  1800 Journal homepage: http://iaesjournal.com/online/index.php/IJECE Hybrid Approach for Prediction of Cardiovascular Disease Using Class Association Rules and MLP K. Srinivas 1 , B. Ramasubba Reddy 2 , B. Kavitha Rani 1 , Ravindar Mogili 3 1 Professor, Jyothishmathi Institute of Technology & Science, Karimnagar, TS, India 2 Professor, SVEC, Tirupati, AP, India 3 Associate Professor, Jyothishmathi Institute of Technology & Science, Karimnagar, TS, India Article Info ABSTRACT Article history: Received Dec 28, 2015 Revised Feb 26, 2016 Accepted Mar 10, 2016 In data mining classification techniques are used to predict group membership for data instances. These techniques are capable of processing a wider variety of data and the output can be easily interpreted. The aim of any classification algorithm is the design and conception of a standard model with reference to the given input. The model thus generated may be deployed to classify new examples or enable a better comprehension of available data. Medical data classification is the process of transforming descriptions of medical diagnoses and procedures used to find hidden information. Two experiments are performed to identify the prediction accuracy of Cardiovascular Disease (CVD).A hybrid approach for classification is proposed in this paper by combining the results of the associate classifier and artificial neural networks (MLP). The first experiment is performed using associative classifier to identify the key attributes which contribute more towards the decision by taking the 13 independent attributes as input. Subsequently classification using Multi Layer Perceptrons (MLP) also performed to generate the accuracy of prediction using all attributes. In the second experiment, identified key attributes using associative classifier are used as inputs for the feed forward neural networks for predicting the presence or absence of CVD. Keyword: Artificial neural networks Associative classifier Classification CVD Copyright © 2016 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: 1. INTRODUCTION With the ever-growing complexity in recent years, huge amounts of information in the area of medicine have been saved every day in different electronic forms such as Electronic Health Records (EHRs) and registers which is used for different purposes. Cardiovascular disease (heart disease) [1]-[3] referred as CVD is the class of diseases that involve the heart or blood vessels. It is essential to evaluate the presence or absence of cardiovascular disease (CVD) risk. Several methods are discussed by researchers to improve cardiovascular risk prediction. The data of the patients collected from different sources is stored in registers and mainly used for monitoring and analyzing health conditions. The existence of accurate epidemiological registers a basic prerequisite for monitoring and analyzing health and social conditions in the population. They are frequently used for research, evaluation, planning and other purposes by a variety of users in terms of analyzing and predicting the health status of individuals. Data Mining aims at discovering knowledge out of data and presenting it in a form that is easily compressible to humans. It is a process that is developed to examine large amounts of data routinely collected. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined