BMC Health Services Research

Table 2 Performance accuracy comparison of using five different machine learning algorithms with all features to predict patient wait times

From: Interpretable machine learning models for prolonged Emergency Department wait time prediction

	CVLR		Random Forest		XGBoost		ANN		SVM
	Training	Testing	Training	Testing	Training	Testing	Training	Testing	Training	Testing
Accuracy	0.75	0.74	0.76	0.74	0.75	0.75	0.75	0.74	0.75	0.75
Recall	0.80	0.80	0.80	0.79	0.79	0.79	0.80	0.79	0.80	0.80
Precision	0.71	0.71	0.73	0.71	0.71	0.71	0.72	0.71	0.71	0.71
F1 score	0.75	0.75	0.76	0.75	0.75	0.75	0.75	0.75	0.75	0.75
AUROC	0.81	0.81	0.81	0.81	0.81	0.81	0.82	0.81	0.79	0.78

The performance accuracy was reported with the use of training and testing data
Abbreviations: XGBoost eXtreme Gradient Boosting, CVLR Cross Validation Logistic Regression, ANN Artificial Neural Network, SVM Support Vector Machine, AUROC Areas Under the Receiver Operating Characteristics

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ISSN: 1472-6963

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