Classification of coronary artery disease data sets by using a deep neural network

Abstract

In this study, a deep neural network classifier is proposed for the classification of coronary artery disease medical data sets. The proposed classifier is tested on reference CAD data sets from the literature and also compared with popular representative classification methods regarding its classification performance. Experimental results show that the deep neural network classifier offers much better accuracy, sensitivity and specificity rates when compared with other methods. The proposed method presents itself as an easily accessible and cost-effective alternative to currently existing methods used for the diagnosis of CAD and it can be applied for easily checking whether a given subject under examination has at least one occluded coronary artery or not.

Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

“…The number of publications on this phenomenon decreased from 12 in 2019 to 7 in 2020. CAD presence prediction 23 [25] Heart disease prediction 5 [26] Coronary heart disease prediction 24 [27] Heart disease prediction 6 [28] CHD detection 25 [29] CHD prediction 7 [30] CAD prediction 26 [31] CHD prediction 8 [32] predict coronary heart disease 27 [33] prediction of CHD 9 [16] CHD Prediction based on risk factors 28 [34] classification of coronary artery disease medical data sets [1] Accuracy of ML algorithms for predicting clinical events 29 [35] Prediction of CHD [17] methodology of predicting CHD 30 [36] CAD detection [37] CAD detection 31 [2] CHD Prediction [38] prediction of heart diseases 32 [39] Heart Disease Diagnosis [40] prediction of heart diseases 33 [41] CHD prediction [42] CAD diagnosis 34 [43] CHD prediction [44] Prediction of CHD 35 [45] NN-based prediction of CHD [46] Diagnosing CHD 36 [47] Prediction of CHD [48] prediction of heart disease 37 [49] Prediction of CHD [50] CHD Diagnosis…”

Detection of Coronary Artery Using Novel Optimized Grid Search-based MLP

“…The research community has explored various ECG-based and data mining-based approaches [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] to detect cardiac diseases. Deep learning has shown a noticeable enhancement in the detection and analysis of cardiac disease [7]. In [8], a convolutional neural network (CNN) was used to detect cardiac disease using an electrocardiogram (ECG).…”

Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals

“…From Table 1, it is observable that most authors developed deep CNN models [35], [37], [40], [41], [43], [46], [47], [57], [59], [61] for the automated classification of MI/CAD/CHF and normal classes while few authors developed hybrid deep models using CNN [39], [42], [45], [51], [53], [18]. Fewer authors employed other deep models such as the deep belief model [48], autoencoders [49], deep multilayer perceptron [52], deep ensemble models [56], deep neural network [60] and long-short term memory model(LSTM) [54] and conventional machine learning classifiers such as artificial neural networks [33], [34], [36], [39], [58] for the classification. High classification accuracies of about 95% were achieved when integral features were extracted using neural networks in [33] and from CNN models [35] [47].…”