978-1-5090-4815-1/17/$31.00 ©2017 IEEE Evaluating Ensemble Prediction of Coronary Heart Disease using Receiver Operating Characteristics Tahira Mahboob 1 , Rida Irfan 2 , Bazelah Ghaffar 3 Department of Software Engineering Fatima Jinnah Women University, The Mall, Rawalpindi, Pakistan tahira.mahboob@yahoo.com Abstract- Heart diseases may perhaps consequence in debility, severe disorder, and meager quality of lifespan. Furthermore, it could also be lethal. Hence inferring heart disease has turn into foremost distress currently. This paper centers on various machine learning practices which assist ascertaining and perceiving innumerable heart diseases. Multifarious machine learning approaches conversed here are Hidden Markov Models, Support Vector Machine, Feature Selection, Computational intelligent classifier, prediction system, data mining techniques and genetic algorithm. Scrutinizing each approach thoroughly allowed us to select most apposite one. This ultimately permits us to propose an Ensemble Model exploiting pertinent machine learning procedures which perfectly categorizes diverse heart diseases. The evaluation of the proposed technique has been conducted using state of the art technology. The proposed technique has an accuracy of 94.21%, a ROC (Receiver Operating Characteristics) of 0.981, RMSE (Root Mean Square Error) of .2568, Precision of 0.953; showing significant improvement when compared to the performance of K-Nearest Neighbor, Artificial Neural Networks and Support Vector Machines algorithms. Analysis/Evaluation of the implemented algorithms and the proposed Ensemble Model has been done expending the Receiver Operator Characteristics. Keywords- ANN (Artificial Neural Networks), Ensemble Model KNN (K-Nearest Neighbor), SVM (Support Vector Machines), ROC. I. INTRODUCTION In developed and under developing countries, prominent origin of death is heart disease. Person’s health is significantly influenced by the heart disease suffered. Cardiovascular disease (CVD) is endured by 80 000 000 inhabitant, alone in united states. Each day approximately 2400 Americans die because of this disease. One very common form of CVD is Anomalous heart rhythm termed as cardiac arrhythmia. The correct functionality of the heart is significantly influenced by Cardiac Autonomic Neuropathy (CAN). Deposits of fatty acids in coronary artery may constrict it down and result in coronary heart disease, which grounds for an occurrence of 1.2 million heart attacks each year. Providing eminent services is the major concern faced by the health care administrations currently. For instance, it requires early diagnosis of heart disease efficiently and effectively. Hence in order to accomplish this task we are executing various heart disease prediction mechanisms followed by proposing Ensemble models. As the irregular heartbeats are easily perceived by electrocardiogram, therefore ECG seems to be quiet helpful for physicians particularly for the bulky volumes of statistics. This Research paper is systemized in following manner. Section-I is the introduction. Section-II summarizes all the research papers reviewed. Section-III converses the implementation of Ensemble Model along with data set and result analysis, followed by concluding the research paper in fourth section. II. LITERATURE REVIEW Expending Artificial Neural Network for prediction of heart disease is major focus of Wijaya et. al[1]. Moreover, Support Vector is also being considered for the prediction process. Predicting heart syndrome is possible within a year by overviewing irregular heart rate. Utilizing various tools such as smart mirror, smart chair, smart mouse and smart phone, data regarding individuals is collected in a server. This is how fatality rate along with number of patients suffering from heart disease decrements significantly. However, in Year 2011 observed accuracy of ANN was 80.06% while SVM observed accuracy was 84.12%. Chen et. al. in 2011[2] present Diagnosis of heart syndrome depends upon the medical data. Heart syndrome prediction system is developed that can help the medical professionals in predicting the heart syndrome by analyzing the medical data of patients. The system takes thirteen medical attributes as input. Then system uses the ANN technique for categorizing the heart syndrome on the basis of these medical attributes. Moreover, ROC is displayed that depicts the performance of 110