Fig. 4 | Scientific Reports

Fig. 4

From: Development and validation of interpretable machine learning models to predict distant metastasis and prognosis of muscle-invasive bladder cancer patients

Fig. 4

Evaluation of the ML models in the internal validation set. (A) ROC curves of different ML models in the internal validation set. (B) AUC values of different ML models in the internal validation set. (C) The performance of 13 ML models in terms of AUC, accuracy, sensitivity, specificity, precision, cross-entropy, Brier scores, Balanced Accuracy (bacc) and F1 Score in the internal validation set. (D) PR curves of different ML models in the internal validation set. (E) Calibration curves of different ML models in the internal validation set. (F) DCA curves of different ML models in the internal validation set. ML, machine learning; CAT, categorical boosting; LR, logistic regression; DT, decision tree; RF, random forest; XGB, extreme gradient boosting; GBM, gradient boosting machine; NB, Naive Bayes; LDA, linear discriminant analysis; QDA, quadratic discriminant analysis; NNET, neural network; GLMNET, generalized linear models with elastic net regularization; SVM, support vector machine; KNN, k-nearest neighbor.

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