Table 9.

Best results of this study compared to previous studies in the classification of different types of myocardial infarction (classification of myocardial infarction)

Researcher	Year	Method	Database	Specificity (%)	Accuracy (%)	Sensitivity (%)
Lui and Chow[11]	2018	Classification using convolutional neural networks and recursive neural networks	PhysioNet PTB	97.2	92.4	97.7
Chang et al.[12]	2012	Extracted statistical, HMM and GMM features Classification based on Markov and Gaussian models	Clinical ECG data	79.82	82.50	85.71
Safdarian et al.[28]	2014	Extracted T-wave integral and total integral of one ECG cycle features Classification using PNN, KNN, MLP neural network, naive bayes methods	PhysioNet PTB	-	76.67	-
Baloglu et al.[13]	2019	Classification based on deep neural networks	PhysioNet PTB	-	99	-
Proposed method	2020	GOA-SVM-liner		100±0	75.5±0.2	100
		GOA-SVM-RBF	PhysioNet PTB	98.72±0.02	80±0.02	100±0
		GOA-SVM-polynomial		97.37±0	94.2±0.2	100±0

PTB – Physikalisch-Technische Bundesanstalt; SVM – Support vector machine; GOA – Grasshopper optimization algorithm; ECG – Electrocardiogram; PNN – Probabilistic neural network; KNN – K-nearest neighbor; MLP – Multilayer perceptron; HMM – Hidden Markov model; GMM – Gaussian mixture model, RBF – Radial basis function