Constructing a prediction model for delayed wound healing after gastric cancer radical surgery based on three machine learning algorithms

Table 4 Comparison of the three machine learning models

Datasets	Prediction models	Precision	Accuracy	Recall	F1 index	Area under the receiver operating curve (95%CI)	P value1	P value2
Training set	Decision tree	0.951	0.917	0.937	0.944	0.962 (0.944-0.979)	4.00 × 10-37	0.4233
	Logistic regression	0.848	0.823	0.930	0.887	0.924 (0.897-0.950)	1.40 × 10-30	< 0.0015
	SVM	0.861	0.845	0.944	0.901	0.749 (0.697-0.802)	1.21 × 10-08	< 0.0014
Validation set	Decision tree	0.940	0.901	0.932	0.936	0.951 (0.920-0.979)	2.72 × 10-21	0.0013
	Logistic regression	0.869	0.855	0.957	0.911	0.937 (0.900-0.970)	4.86 × 10-20	< 0.0015
	SVM	0.890	0.875	0.958	0.922	0.773 (0.685-0.855)	7.30 × 10-07	< 0.0014

¹P value represents the significance test results of area under the receiver operating curves performance between the three models and the random model.

²P value indicates the results of significance tests for area under the receiver operating curves comparisons between the models.

³P value comparison between the decision tree and logistic regression.

⁴P value comparison between the decision tree and the support vector machine.

⁵P value comparison between the logistic regression and the support vector machine.

SVM: Support vector machine; CI: Confidence interval.