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Fig. 1 | BMC Gastroenterology

Fig. 1

From: Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis

Fig. 1

The ROC curves of different models. a The ROC curves of different combinations of features from SVM for predicting MOF in MSAP and SAP. AUC of the optimal combination = 0.840 (95% CI: 0.783–0.896); AUC of single feature (BUN) = 0.702 (95% CI: 0.625–0.778); AUC of all features = 0.816 (95% CI: 0.755–0.876). b The ROC curves of different combinations of features from LRA for predicting MOF in MSAP and SAP. AUC of the optimal combination = 0.832 (95% CI: 0.773–0.890); AUC of single feature (IL-6) = 0.709 (95% CI: 0.642–0.775); AUC of all features = 0.783 (95% CI: 0.714–0.853). c The ROC curves of different combinations of features from ANN for predicting MOF in MSAP and SAP. AUC of the optimal combination = 0.834 (95% CI: 0.777–0.890); AUC of single feature (IL-6) = 0.705 (95% CI: 0.639–0.772); AUC of all features = 0.789 (95% CI: 0.723–0.856). d The ROC curves of three models and the APACHE II score for predicting MOF in MSAP and SAP. AUC of SVM = 0.840 (95% CI: 0.783–0.896); AUC of LRA = 0.832 (95% CI: 0.773–0.890); AUC of ANN = 0.834 (95% CI: 0.777–0.890); AUC of APACHE II score = 0.814 (95% CI: 0.759–0.869)

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BMC Gastroenterology

ISSN: 1471-230X

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