Table 3. ML classifiers used to evaluate risk assessment and make predictions.

Authors	ML algorithms	Aim	Performance
Przewlocka-Kosmala, et al.[33]	Cluster analysis	To identify prognostic phenotypes among patients with heart failure and preserved ejection fraction	Lower left ventricle systolic reserve may have a prognostic role in heart failure and preserved ejection fraction
Kwon, et al.[34]	Deep neural network	To detect in-hospital cardiac arrest and death without attempted resuscitation	AUC: 0.85
			AUPRC: 0.044
Daghistani, et al.[35]	Random forest	To predict in-hospital length of stay among cardiac patients	Random forest outperformed among the other models with
	Artificial neural network		Sensitivity: 80%
	Support vector machine		Accuracy: 80%
	Bayesian network		AUC: 0.94
Mortazavi, et al.[36]	Logistic regression	To predict 30-day all-cause of hospitalreadmission of patients with heart failure	Random forest outperformedclassical statistical methods
	Poisson regression
	Random forest
	Boosting
Bhattacharya, et al.[37]	Logistic regression	To assess the risk of ventricular arrhythmia in hypertrophic cardiomyopathy	Sensitivity: 0.73
	Naıve bayes		Specificity: 0.76
	Decision tree		AUC: 0.83
	Random forest
Alaa, et al.[38]	Supprto vector machines	To evaluate cardiovascular risk in asymptomatic people	AUC: 0.724
	Random forest
	Neural network
	AdaBoost
	Gradient boosting

AUC: area under the curve; AUPRC: area under the precision recall curve; ML: machine learning.