Heart disease is amongst the most widely recognized diseases in the world. This research aims to consolidate the precision of heart disease classification/diagnosis by developing a system depending on multiple classifiers. The proposed system contains two phases, which are the preprocessing phase and the classification phase. The preprocessing phase includes data cleaning, normalization and accounting for missing values. In the classification phase, multiple classifiers are used as an ensemble technique based on the Multilayer Perceptron (MLP), K-Nearest Neighbor (K-NN) and C4.5. A heart disease dataset, which contains four databases and gathered from the UCI machine learning repository, was used for experiments. The proposed classification system gives 99.4% classification precision according to 10-fold cross-validation technique. The outcome obtained from the proposed system shows that its performance is better than that of already reported classification systems.
heart disease, classification, multilayer perceptron, K-Nearest Neighbor (K-NN), C4.5
 The Mayo Foundation for Medical Education and Research (MFMER). http://www.mayoclinic.org/diseases-conditions/heart-disease/basics/definition/con-20034056, accessed on June 2018.
 The World Health Organization (WHO). http://www.who.int/en/, accessed on June 2018.
 The Centers for Disease Control and Prevention (CDC). https://www.cdc.gov/, accessed on June 2018.
 Haykin S. (2016). Neural networks and learning machines. Pearson Education Dorling Kindersley. 3rd edition.
 Rokach L, Maimon OZ. (2014). Data mining with decision trees: Theory and applications, series in machine perception and artificial intelligence. World Scientific Publishing Company, 2nd Edition.
 Manimekalai K. (2016). Prediction of heart diseases using data mining techniques. International Journal of Innovative Research in Computer and Communication Engineering 4(2): 2161-2168. https://doi.org/10.17485/ijst/2016/v9i39/102078
 Abdar M, Kalhori SRN, Sutikno T, Subroto IMI, Arji G. (2015). Comparing performance of data mining algorithms in prediction heart diseases. International Journal of Electrical and Computer Engineering (IJECE) 5(6): 1569-1576.
 Ratnaparkhi D, Mahajan T, Jadhav V. (2015). Heart disease prediction system using data mining technique. International Research Journal of Engineering and Technology (IRJET) 2(8): 1553-1555.
 Dewan A, Sharma M. (2015). Prediction of heart disease using a hybrid technique in data mining classification. Proceedings of IEEE 2nd International Conference on Computing for Sustainable Global Development, pp. 704-706.
 Karthiga G, Preethi C, Devi RDH. (2014). Heart disease analysis system using data mining techniques. International Journal of Innovative Research in Science Engineering and Technology 3(Sp.3): 3101-3105.
 Patil RR. (2014). Heart disease prediction system using naïve bayes and Jelinek-mercer smoothing. International Journal of Advanced Research in Computer and Communication Engineering 3(5): 6787-6789.
 The UCI Machine learning Repository, Center for Machine Learning and Intelligent Systems, Heart Disease Data Set. http://archive.ics.uci.edu/ml/datasets/heart+Disease, accessed June 2018.
 Du KL, Swamy MNS. (2014). Neural networks and statistical learning. Springer.
 Harrington P. (2012). Machine learning in action. Manning Publications, 1st Edition.
 Hssina B, Merbouha A, Ezzikouri H, Erritali M. (2014). A comparative study of decision tree ID3 and C4.5. International Journal of Advanced Computer Science and Applications, Special Issue on Advances in Vehicular Ad Hoc Networking and Applications, pp. 13-19.
 Viswanathan S, Viswanathan V. (2015). R data analysis cookbook. Packt Publishing.
 Rani KU. (2011). Analysis of heart diseases dataset using neural network approach. International Journal of Data Mining and Knowledge Management Process (IJDKP) 1(5): 1-8. https://doi.org/10.5121/ijdkp.2011.1501
 Meda S, Bhogapathi RB. (2018). Identification of heart disease using fuzzy neural genetic algorithm with data mining techniques. Advances in Modelling and Analysis B 61(2): 99-105. https://doi.org/10.18280/ama_b.610208