Table 3 Overfitting evaluation of the prediction models.

From: Study on the prognosis predictive model of COVID-19 patients based on CT radiomics

Models

AUC [95%CI]

P-value

Training cohort

Test cohort

LR

0.886 [0.817–0.936]

0.845 [0.732–0.924]

0.4426

SVM

0.845 [0.769–0.904]

0.829 [0.713–0.912]

0.8556

DT

0.832 [0.754–0.893]

0.827 [0.711–0.911]

0.7253

RF

0.812 [0.769–0.904]

0.843 [0.729–0.922]

0.5767

XGBoost

0.858 [0.784–0.914]

0.836 [0721–0.917]

0.7146

Clinical model

0.730 [0.642–0.807]

0.805 [0.686–0.894]

0.2854

Combined model

0.921 [0.858–0.962]

0.865 [0.757–0.938]

0.3245

  1. P-value reflected the differences between the training and test cohorts, and P < 0.05 (two-sided) were considered statistically significant.
  2. AUC area under the curve; CI confidence interval; LR logistic regression; SVM support vector machine; DT decision tree; RF random forest; XGBoost extreme gradient boosting.
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