Application of machine learning models in predicting the risk of thromboembolic events in patients with nonvariceal gastrointestinal bleeding

Table 3 Characteristics of machine learning models in the external validation sets, mean (95%CI)

Model	Accuracy	Precision	Sensitivity	Specificity	F1	Area under the receiver operating characteristic curve	P value
L1 regularized logistic regression	0.78 (0.728-0.825)	0.395 (0.296-0.504)	0.711 (0.566-0.823)	0.793 (0.737-0.84)	0.508	0.805 (0.735-0.875)	< 0.01
Support vector machines	0.77 (0.717-0.815)	0.389 (0.295-0.492)	0.778 (0.637-0.875)	0.768 (0.71-0.817)	0.519	0.806 (0.727-0.884)	< 0.01
Categorical boosting	0.826 (0.778-0.866)	0.466 (0.343-0.592)	0.6 (0.455-0.73)	0.869 (0.82-0.906)	0.524	0.815 (0.746-0.885)	< 0.01
Random forest	0.738 (0.683-0.785)	0.344 (0.255-0.445)	0.711 (0.566-0.823)	0.743 (0.683-0.794)	0.464	0.804 (0.736-0.872)	< 0.01
Extreme gradient boosting	0.727 (0.672-0.776)	0.318 (0.23-0.421)	0.622 (0.476-0.749)	0.747 (0.688-0.798)	0.421	0.746 (0.661-0.831)	< 0.01
D-dimer	0.734 (0.68-0.782)	0.258 (0.166-0.379)	0.356 (0.232-0.502)	0.806 (0.751-0.851)	0.299	0.51 (0.403-0.617)	-

P value reflected the comparisons between each machine learning model and D-dimer.