Hybrid ensemble framework for heart disease detection and prediction

Abstract

Data mining techniques have been widely used in clinical decision support systems for detection and prediction of various diseases. As heart disease is the leading cause of death for both men and women, detection and prediction of the heart disease is one of the most important issues in medical domain and many researchers developed intelligent medical decision support systems to improve the ability of the CAD systems in diagnosing heart disease. However, there are almost no studies investigating capabilities of hybrid ensemble methods in building a detection and prediction model for heart disease. In this work, we investigate the use of hybrid ensemble model in which a more reliable ensemble than basic ensemble models is proposed and leads to better performance than other heart disease prediction models. To evaluate the performance of proposed model, a dataset containing 278 samples from SPECT heart disease database is used that after applying the model on the data, 96% of classification accuracy, 80% of sensitivity and 93% of specificity are obtained that indicates acceptable performance of the proposed hybrid ensemble model in comparison with basic ensemble model as well as other state of the art models.