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Fig. 4 | BioData Mining

Fig. 4

From: Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES

Fig. 4

ROC and AUC of models use both oculomics and clinic variables as input variables. a-c The performance of our model using TyG-index=8.0, 8.75, or 8.93 as the cut-off point in the internal validation dataset is demonstrated. d-f The performance of our model using TyG-index=8.0, 8.75, or 8.93 as the cut-off point in the external validation dataset is demonstrated. AUC, Area under curve. TyG, Triglyceride-glucose index.ROC, Receiver operating characteristic

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BioData Mining

ISSN: 1756-0381