Fig. 3: Performance of four Bayesian models (Bayes-splenic hilum lymph node metastasis [SHLNM], Bayesian least absolute shrinkage and selection operator, basic, and Student-T models) and one frequentist model (frequentist logistic regression model) based on the average results from 5-fold cross-validation, along with the 95% confidence intervals.
From: Establishment of a machine learning model for predicting splenic hilar lymph node metastasis
a The receiver operating characteristic-area under the curve (AUC) compares the true positive rate (sensitivity) and false positive rate for each model. The Bayes-SHLNM model achieves the highest AUC of 0.83 [95% confidence interval (CI), 0.74–0.91]. The shaded areas around each curve represent the 95% confidence intervals for each model. b The precision-recall curve evaluates model performance with respect to precision and recall. The Bayes-SHLNM performed best among four Bayesian models, with an AUC of 0.35 [95% CI, 0.14–0.56], which was comparable with the FLR model (AUC = 0.37 [95% CI, 0.13–0.61]). The shaded regions represent the 95% CIs.
