CORONARY HEART DISEASE CLASSIFICATION USING IMPROVED PENGUIN EMPEROR OPTIMIZATION-BASED LONG SHORT TERM MEMORY NETWORK

Abstract

Ventricular fibrillation (VF) is the most life-threatening and dangerous type of Cardiac Arrhythmia (CA), with a mortality rate of 10-15% in a year. Therefore, early detection of cardiac arrhythmia is important to reduce the mortality rate. Many machine learning algorithms have been proposed and have proven their usefulness in the classification and detection of heart problems. In this research manuscript, a novel Long Short Term Memory (LSTM) classifier with Improved Penguin Optimization (IPEO) is implemented for VF classification. The IPEO is used in finding optimal hyperparameters that overcome the overfitting problem. The presented model is tested, trained, and validated using two standard datasets that are available publicly: Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) and the China Physiological Signal Challenge (CPSC) 2018 dataset. Both of them consist of ECG recordings for five seconds of coronary heart disease (CHD) patients. Furthermore, Fuzzy C-Means and Enhanced Fuzzy Rough Set method (FCM-ETIFRST) are used for feature selection to extract informative features and to cluster membership degree, non-membership degree, and hesitancy degree. On the MIT-BIH dataset, the proposed model achieved accuracy, sensitivity, specificity, precision, and Matthews’s correlation coefficient (MCC) of 99.75%, 98.29%, 98.39%, 98.35%, and 97.79% respectively. On the CPSC 2018 dataset, the proposed model achieved accuracy of 99.79%, sensitivity of 99.11%, specificity of 98.20%, precision of 99.43%, and MCC of 98.57%. Hence, the results proved that the proposed method provides better results in the classification of VF.