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

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