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Table 9 Number of selected features and corresponding model performance

From: A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example

Model No. of features Testing discrimination AŨC (CI, CI%) Training discrimination AŨC Generalization ΔAŨC% Calibration HL-p
BL 8 0.778 (0.722–0.831, 14.0%) 0.815 4.5% 0.65*
BQ 3 0.785 (0.738–0.832, 12.0%) 0.780 -0.6% 0.19*
k NN 5 0.772 (0.717–0.822, 13.6%) 0.792 2.5% 0.01*
LR 14 0.781 (0.721–0.830, 14.0%) 0.827 5.6% 0.29
HS 14 0.768 (0.714–0.821, 13.9%) 0.828 7.2% <0.001*
DS 16 0.779 (0.727–0.830, 13.2%) 0.836 6.8% <0.001*
ANN1 13 0.776 (0.715–0.827, 14.4%) 0.843 7.9% 0.07*
ANN2 10 0.778 (0.726–0.825, 12.7%) 0.837 7.0% 0.01*
  1. *after recalibration
  2. BL, Bayes linear; BQ, Bayes quadratic; kNN, k-nearest neighbour; LR, logistic regression; HS, Higgins score; DS, direct score; ANN1, one-layer artificial neural network; ANN2, two-layer artificial neural network; AŨC, median value of area under receiver operating characteristic curve calculated from 1000 bootstrap samples; ΔAŨC%, difference between AŨC of training and test data; CI and CI%, confidence interval and percentage confidence interval; HL-p, p-value of the Hosmer-Lemeshow goodness-of-fit test.