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Table 2 Area under the receiver operating characteristic curve prediction results predictors at varied stages of pregnancy

From: Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study

Predictor levelsa

Dataset

AUC (95% CI)

CART

LASSO regression

Simple super learnerb

Complex super learnerc

1

Discovery set

0.613 (0.603–0.622)

0.670 (0.663–0.676)

0.673 (0.667–0.679)

0.683 (0.676–0.689)

Validation set

0.592 (0.567–0.616)

0.634 (0.615–0.653)

0.635 (0.615–0.654)

0.634 (0.615–0.653)

1, 2

Discovery set

0.618 (0.609–0.628)

0.685 (0.678–0.691)

0.688 (0.682–0.695)

0.761 (0.756–0.767)

Validation set

0.588 (0.563–0.613)

0.647 (0.628–0.666)

0.645 (0.626–0.664)

0.648 (0.630–0.667)

1, 2, 3

Discovery set

0.740 (0.732–0.748)

0.785 (0.780–0.791)

0.790 (0.785–0.796)

0.869 (0.865–0.873)

Validation set

0.703 (0.682–0.724)

0.750 (0.733–0.767)

0.749 (0.733–0.766)

0.754 (0.739–0.772)

1, 2, 3, 4

Discovery set

0.785 (0.777–0.792)

0.849 (0.845–0.854)

0.852 (0.848–0.857)

0.934 (0.931–0.936)

Validation set

0.745 (0.722–0.767)

0.809 (0.794–0.823)

0.808 (0.794–0.823)

0.815 (0.800–0.829)

  1. AUC, area under the receiver operating characteristic curve; CART, classification and regression tree; LASSO, least absolute shrinkage and selection operator
  2. aLevel 1: 1-year preconception to last menstrual period; level 2: last menstrual period to before diagnosis of gestational diabetes; level 3: at the time of diagnosis of gestational diabetes; level 4: 1 week after diagnosis of gestational diabetes
  3. bCandidate algorithms in simple super learner included response-mean, LASSO regression, and CART
  4. cCandidate algorithms in complex super learner included response-mean, LASSO regression, CART, random forest, and extreme gradient boosting
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BMC Medicine

ISSN: 1741-7015

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