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. 2020 Dec 15;2(5):690–706. doi: 10.1016/j.jaccao.2020.11.004

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© 2020 The Authors

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Accuracy Measures of Prediction Models Using Random Forest

Boxplots representing the accuracy measures for the overall dataset, the testing set, and the training set for the (A) clinical model, (B) genetic model, (C) combined model, and (D) receiver operator characteristic AUC for model-derived prediction of anthracycline cardiotoxicity. Clinical model AUC: 0.59 (black), genetic model AUC: 0.71 (blue), combined model AUC: 0.72 (red). AUC = area under the curve; FNR = false negative rate; FPR = false positive rate; MC = misclassification; NPV = negative predictive value; PPV = positive predictive value; Sens = sensitivity; Sn = sensitivity; Sp = specificity; Spec = specificity.

Accuracy Measures of Prediction Models Using Random Forest

Boxplots representing the accuracy measures for the overall dataset, the testing set, and the training set for the (A) clinical model, (B) genetic model, (C) combined model, and (D) receiver operator characteristic AUC for model-derived prediction of anthracycline cardiotoxicity. Clinical model AUC: 0.59 (black), genetic model AUC: 0.71 (blue), combined model AUC: 0.72 (red). AUC = area under the curve; FNR = false negative rate; FPR = false positive rate; MC = misclassification; NPV = negative predictive value; PPV = positive predictive value; Sens = sensitivity; Sn = sensitivity; Sp = specificity; Spec = specificity.