Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.
Skip to main content

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.
Back to article page

BMC Medical Informatics and Decision Making

ISSN: 1472-6947

Contact us