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Fig. 5 | BioData Mining

Fig. 5

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

Fig. 5

ROC and AUC of models use both oculomics and clinics as input variables. a-b The performance of our model using AIP=0.318 or 0.34 as the cut-off point in the internal validation dataset is demonstrated. c-d The performance of our model using AIP=0.318 or 0.34 as the cut-off point in the external validation dataset is demonstrated. AIP, Atherogenic index of plasma. AUC, Area under curve. ROC, Receiver operating characteristic

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BioData Mining

ISSN: 1756-0381