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Table 1 Summary of the baseline characteristics and the outcomes reported in ML studies provide data for risk stratification purposes

From: Machine learning techniques for arrhythmic risk stratification: a review of the literature

First author Year of publication Enrollment period Number of patients Age (years) Males (%) Type of clinical setting Machine learning technique Performance Type of predicted arrhythmia Features used for the prediction model
Hypertrophic cardiomyopathy
Alis [24] 2020 2014–2019 64 48.13 ± 13.06 65.6 HCM SVM, naïve Bayes, RF, k-NN Accuracy
SVM: 82.8%
Naïve Bayes: 83.3%
RF: 82.8%
k-NN: 93.8%
Ventricular tachyarrhythmia mean LGE%
Bhattacharya [19] 2019 Mean duration: 2.86 years 711 No VA: 54 ± 15
Yes VA:
49 ± 16
61.0 HCM ensemble of logistic regression and natıve Bayes classifiers Sens: 73%, spe: 76%, C-index = 0.83 Sustained VT, VF 22 features identified as predictive of VAr by the HCM-VAr-Risk Model
Lyon [25] 2018 N/A 123 47 ± 15 66.0 HCM Density-based clustering algorithm N/A VA Data from 12 lead ECG holter
Smole [20] 2021 N/A 2302 46 ± 19 62.9 HCM RF, XGBoost, SVM, NN Accuracy
RF: 72%
SVM (linear): 69%
Bosted trees: 75%
NN: 74%
F1 score
RF: 0.68
SVM (linear): 0.63
Boosted trees: 0.72
NN: 0,68
VT demographic characteristics, genetic data, clinical investigations, medications and disease-related events
Ischemic heart disease—heart failure—cardiomyopathies
Kotu [27] 2015 N/A 54 N/A N/A Post-myocardial infarction patients K-NN, SVM, decision tree, RF SVM classifier provided average accuracy of 92.6% and AUC of 0.921 VA Quantitative discriminative features extracted from LGE-CMR image
Rogers [29] 2021 N/A 42 64.7 ± 13.0 97.6 Ischemic CMP SVM, CΝΝ Accuracy of the SVM: 83.2%
Accuracy of convolutional NN: 56.7%
VT/VF Ventricular monophasic action potentials
Okada [28] 2021 2003–2015 122 60 ± 11 87.0 Ischemic CMP SVM Accuracy: 81%
Correct classification: 86%
NPP: 91%
VA LGE-CMR data
Au-yeung [67] 2018 NA 788 60 77.3 HF RF, SVM Both RF and SVM methods achieve a mean AUC of 0.81 for 5-min prediction and mean AUC of 0.87–0.88 for 10- second prediction Ventricular tachyarrhythmia VA onset prediction with heart rate variability data from ICD
Meng [32] 2019 2017–2019 Retrospective: 500
Prospective: 1000
N/A N/A HFrEF Information gain ranking, decision trees, logistic regression, SVM, RF, ANN NA (study protocol) VT/VF Demographic, clinical, biological, electrophysiological, social and psychological variables
Wu [33] 2020 2003–2015 382 57 ± 13 72.0 HFrEF RF AUC: 0.88 VA clinical heart failure course, baseline CMR imaging metrics, levels of the interleukin-6
Rocon [37] 2020 2011–2017 108 38.3 ± 15.5 48.1 Non-compaction CMP SVD impute, Parameter Selection Algorithm, sequential forward selection, distance-weighted k-NN Accuracy: 75.5%
Sens: 77%
Spe: 75%
Major adverse cardiovascular events LVEF (by CMRI), RV end systolic volume (by CMRI), RV systolic dysfunction (by echo) and RV lower diameter (by CMRI)
Implantable devices
Marzec [68] 2018 Minimum one year of observation 235 N/A N/A Patients with implantable electronic devices Naïve, RF, decision tree analysis, k-NN, SVM Accuracy
Naïve: 74.5%
RF: 76.6%
Decision tree: 70.2%
K-NN: 72.3%
SVM: 74.5%
F1 score
Naïve: NA
RF: 0.27
Decision tree: 0.13
K-NN: 0.00
SVM: NA
VT Data about physical daily activities
Shakibfar [35] 2019 N/A 19,935 N/A N/A ICD patients RF Accuracy: 96%
AUC: 0.80
VA (Electrical Storm) ICD variables
Channelopathies
Lee [43] 2021 1997–2017 516 50 ± 16 92.0 Brugada syndrome Nonnegative matrix factorisation, RF Random survival forest
Precision: 83.4%
Recall: 85.3%
F1 score: 84.3%
Nonnegative matrix factorization
Precision: 87.1%
Recall: 88.8%
F1 score: 87.9%
VT/VF,
Brugada syndrome
Clinical and electrocardiographic data
Congenital heart disease
Dilller [51] 2020 10 year observation period 372 16.0 54.8 Tetralogy of Fallot Deep learning algorithms N/A VA prognosis, cardiac death, cardiac arrest Prediction of adverse outcome using CMR data
Sun [52] 2021 2009 –2019 269 N/A 35.7 Following ASD closure in pediatric patients RF, SVM, K-nearest neighbor, AdaBoost, decision tree Accuracy
k-NN: 78.9%
Decision tree: 82.6%
AdaBoost: 84.95%
SVM: 89.3%
RF: 94.7%
AUC
k-NN: 0.82
Decision tree: 0.80
AdaBoost: 0.75
SVM: 0.87
RF: 0.90
Postoperative arrhythmias Demographic characteristics, cardiac imaging data and blood exams
Different clinical settings
Sessa [49] 2020 January 2015–December 2016 18,018 74.8 39.9 Geriatric patients Conditional inference tree NA VA Specific medications
Taye [58] 2019 N/A N/A N/A N/A Public databases ANN classifiers Accuracy: 98.6%
Sens: 98.4%
Spe: 99%
Ventricular tachyarrhythmia QRS complex shape
Okada [42] 2019 2000–2017 76 53 ± 10 59.0 Cardiac sarcoidosis Supervised RF Correct classification: 87%
C‐statistic: 0.91
VA, atrioventricular block Regional scar burden
Taye [57] 2020 N/A 78 20.7–75.3 80.8 Spontaneous ventricular tachyar- rhythmia database CNN, k-NN, ANN, SVM Accuracy
CNN: 84.6%
ANN: 73.5%
SVM: 67.9%
k-NN: 65.9%
AUC
CNN: 0.78
ANN: 0.65
SVM: 0.63
k-NN: 0.62
Ventricular tachyarrhythmia Heart rate variability signals
Wu [54] 2020 2012–2016 508 30.83 ± 6.17 75.0 Young hypertensive patients Recursive feature elimination, extreme gradient boosting C statistic: 0.757 Composite endpoint including sustained VT/VF Demographics, medical history, vital signs, echocardiography, polysomnography, blood exams
Bergau [47] 2018 N/A N/A N/A N/A N/A SVM Sens: 85%
Spe: 90%
Torsades de pointes Gene expression differences
Chen [56] 2021 N/A 17 N/A N/A N/A SVM, RF, XGboost RF model with an average precision of 99.99% and recall of 88.98% VA Heartbeat interval time series
Yap [48] 2004 N/A N/A N/A N/A N/A SVM, probabilistic NN, k-NN, decision tree Accuracy
SVM: 97.4%
Probabilistic NN: 71.8%
k-NN: 89.7%
Decision tree: 38.5%
Torsade de pointes Set of agents
Lee [55] 2016 2013–2015 N/A N/A N/A N/A ANN Sens: 88.2% Spe: 82.4% PPV: 83.3% NPV: 87.5% VT one hour prior to its occurrence Heart rate variability and respiratory rate variability
  1. HCM, hypertrophic cardiomyopathy; CMR, cardiac magnetic resonance; LGE, late gadolinium enhancement; VT, ventricular tachycardia; VA, ventricular arrhythmias; VF, ventricular fibrillation; SVM, support vector machine; SCD, sudden cardiac death; ANN, artificial neural network; HFrEF, heart failure with reduced ejection fraction; CMP, cardiomyopathy; ICD, implantable cardioverter-defibrillator; ASD, atrial septal defect; Sens, sensitivity; Spe, specificity; RF, random forest; CNN, convolutional neural network