Fig. 4: ROC curve of YOCRC risk stratification models in both validation datasets. | npj Precision Oncology

Fig. 4: ROC curve of YOCRC risk stratification models in both validation datasets.

From: Development and validation of machine learning models for young-onset colorectal cancer risk stratification

Fig. 4

A The AUC of the ROC curve for YOCRC risk stratification models with different ML algorithms in the internal validation. AUC Area under the ROC curve, ROC Receiver operating characteristic, YOCRC Young-onset colorectal cancer. LR Logistic regression, RF Random Forest, KNN k-Nearest Neighbor, SVC Support Vector Classification, DT Decision Tree, XGBoost eXtreme Gradient Boosting, AdaBoost Adaptive Boosting. B The AUC of the ROC curve for YOCRC risk stratification models with different ML algorithms in the temporal validation. AUC area under the ROC curve, ROC receiver operating characteristic, YOCRC young-onset colorectal cancer, LR logistic regression, RF random forest, KNN k-Nearest Neighbor, SVC Support Vector Classification, DT decision tree, XGBoost eXtreme Gradient Boosting, AdaBoost Adaptive Boosting.

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