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. 2022 Aug 26;12:14593. doi: 10.1038/s41598-022-18650-6

Figure 2.

Figure 2

Receiver operating characteristic curve of prehospital diagnostic algorithms for acute coronary syndrome with 17 features. ROC curves of the top six machine learning algorithms for the prehospital prediction of ACS using 17 features are shown. The ROC curves are depicted at 1-specificity on the x-axis and sensitivity on the y-axis using (a) the training score, (b) the test score, and (c) external cohort score. AUC is presented with 95% confidence interval. ACS (acute coronary syndrome), AUC (area under the receiver operating characteristic curve), CI (confidence interval), LDA (linear discriminant analysis), LR (logistic regression), MLPC (multilayer perceptron classifier), ROC (receiver operating characteristic), SVM (R) (support vector machine radial basis function), VC (voting classifier), XGB (eXtreme Gradient Boosting).