Research Article Poisson Mixture Regression Models for Heart Disease Prediction Chipo Mufudza and Hamza Erol Statistics Department, Cukurova University, 01330 Adana, Turkey Correspondence should be addressed to Chipo Mufudza; chipmuf@gmail.com Received 20 April 2016; Accepted 20 October 2016 Academic Editor: David A. Winkler Copyright © 2016 C. Mufudza and H. Erol. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Early heart disease control can be achieved by high disease prediction and diagnosis efciency. Tis paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two diferent classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Infated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be efectively done by identifying the major risks componentwise using Poisson mixture regression model. 1. Introduction Heart disease encloses a number of conditions that infuence the heart and not just heart attacks. Tese may include functional problems of the heart such as heart-valve abnor- malities, high blood pressure (BP), smoking, diet, cholesterol, or irregular heart rhythms. Problems like these can lead to heart failure, arrhythmia, and a host of other problems. Te work in [1] claims that heart diseases have become the leading global death accounting for 17.3 million deaths per year and killing 1 in 7 in the US alone. Terefore efective and efcient automated heart disease prediction systems can be benefcial to both the patient and cardiologist. Although there has been increasing interests on heart disease problems especially with the use of data mining techniques and algorithms, most of them concentrated on a supervised classifcation approach through diferent classifcations and algorithms. In a comparative approach research by Bagirov et al. [2] they showed that it is possible to classify heart disease prob- lems using either supervised or unsupervised classifcations. Supervised classifcation on diferent patients status by data mining algorithms to predict heart disease has been explored by various authors in machine learning. H. D. Masethe and M. A. Masethe [3] identifed that the predictive accuracy of J48, over REPTREE and CART, was reliable for heart disease prediction in South Africa. Weighted fuzzy models based on supporting have also been studied and analysed showing an improvement against the network based models [4]. Super- vised classifcation algorithms can improve the efciency to cardiologist as shown by Taneja [5] on PGI data where over 7300 observations were classifed using J48 and Naive Bayes in WEKA. In a general review on the heart disease using data mining techniques done by Kaur and Singh [6], they summarised that most researches show that the main risk fac- tors are cholesterol, lack of exercise, obesity, and high blood pressure whilst the best algorithms seem to be dominated by decision trees. Supervised classifcations predictions can also be improved in some cases by incorporating unsupervised classifcation techniques like clustering as a preprocessing procedure. However, it may not always be the case as shown by Soni et al. [7] that decision trees can still outperform the Bayesian classifcation even when clustering is incorporated although they mentioned that both algorithms can be improved by genetic algorithms. Tey also implemented a combination of associative classifcation and genetic algorithms in an efort to come up with the best system Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2016, Article ID 4083089, 10 pages http://dx.doi.org/10.1155/2016/4083089