Fig. 4 | Scientific Reports

Fig. 4

From: Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study

Fig. 4

ROC Curves for XGBoost Model Performance. (A)Training Set ROC Curve: The ROC curve for the XGBoost model on the training set, with an AUC of 0.950 (95% CI: 0.940-0.960).

(B) Validation Set ROC Curve: The ROC curve for the XGBoost model on the validation set, with an AUC of 0.893 (95% CI: 0.860-0.925). (C)Test Set ROC Curve: The ROC curve for the XGBoost model on the test set, with an AUC of 0.890 (95% CI: 0.868-0.913).

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