Application of ensemble classifiers in accurate diagnosis of myocardial ischemia conditions

Myocardial ischemia (MI) is abnormal cardiac condition during which myocardium is deprived of blood and oxygen supply. It is the most prominent heart disease around the globe, and its prevalence increases with age factors. Accurate and timely identification and treatment of MI help to prevent from series of dreadful ischemic heart diseases such as heart attack and heart failure. However, the manual interpretation of electrocardiogram (ECG) for diagnosis of MI is tedious task for physicians. Hence, automated classification is very useful for diagnosis of MI condition. Our study presents an approach for automated diagnosis of MI condition using ECG beats. In this methodology, non-linearity in ECG is finely captured using higher-order statistics (HOS), and then, dimensionality is reduced. Confidence interval of features is verified using Student’s t-test (probability (p)-value <0.05). Decision Tree (DT) as the base classifier following different ensemble classifiers, viz. Adaboost (ADB), Bagging (BGG), Rotation Forest (ROF) and Random Forest (RAF) are subjected for diagnosis of normal sinus rhythm (NSR) against MI conditions. Among these, ROF is diagnosed with 99.51 ± 0.31 % accuracy, 98.82 ± 1.04 % sensitivity and 99.83 ± 0.15 % specificity. In this system, higher specificity rate defines precision in diagnosis of True Negative (TN) condition. Results achieved in this study are efficient with 0.9886 of Matthews correlation coefficient (MCC). Our proposed system is non-invasive and cost-efficient and helps in screening of MI, predominantly in rural and remote areas.