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

Table 2 Characteristics of machine learning models in the internal validation sets, mean (95%CI)

Model	Accuracy	Precision	Sensitivity	Specificity	F1	Area under the receiver operating characteristic curve	P value
L1 regularized logistic regression	0.736 (0.697-0.771)	0.43 (0.362-0.501)	0.716 (0.628-0.79)	0.741 (0.698-0.781)	0.537	0.793 (0.750-0.837)	< 0.01
Support vector machines	0.701 (0.661-0.738)	0.401 (0.340-0.465)	0.802 (0.72-0.864)	0.673 (0.627-0.716)	0.534	0.804 (0.757-0.851)	< 0.01
Categorical boosting	0.754 (0.716-0.789)	0.455 (0.386-0.526)	0.75 (0.664-0.82)	0.755 (0.712-0.794)	0.567	0.818 (0.777-0.859)	< 0.01
Random forest	0.678 (0.638-0.716)	0.38 (0.321-0.443)	0.793 (0.711-0.857)	0.647 (0.6-0.691)	0.514	0.798 (0.755-0.842)	< 0.01
Extreme gradient boosting	0.71 (0.67-0.746)	0.402 (0.338-0.47)	0.724 (0.637-0.797)	0.706 (0.661-0.747)	0.517	0.772 (0.723-0.821)	< 0.01
D-dimer	0.621 (0.579-0.661)	0.309 (0.253-0.371)	0.621 (0.530-0.704)	0.621 (0.574-0.666)	0.413	0.618 (0.552-0.683)	-

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