Machine learning-based radiomic nomogram from unenhanced computed tomography and clinical data predicts bowel resection in incarcerated inguinal hernia

Table 3 Predictive performance of radiomic signature in multiple models

Model	Set	AUC (95%CI)	ACC	SEN	SPE	PPV	NPV
LR	Training	0.828 (0.7523-0.902)	0.832	0.610	0.908	0.694	0.872
	Test	0.791 (0.668-0.915)	0.698	0.933	0.622	0.483	0.958
SVM	Training	0.919 (0.859-0.979)	0.863	0.927	0.842	0.667	0.971
	Test	0.742 (0.599-0.885)	0.717	0.800	0.703	0.500	0.897
KNN	Training	0.840 (0.780-0.901)	0.783	0.756	0.792	0.554	0.905
	Test	0.613 (0.463-0.764)	0.528	0.867	0.417	0.361	0.882
RF	Training	1.000 (0.999-1.000)	0.988	1.000	0.983	0.953	1.000
	Test	0.603 (0.434-0.771)	0.717	0.267	0.944	0.500	0.756
ET	Training	1.000 (1.000-1.000)	1.000	1.000	1.000	1.000	1.000
	Test	0.583 (0.418-0.748)	0.604	0.600	0.622	0.375	0.793
XGBoost	Training	1.000 (1.000-1.000)	1.000	1.000	1.000	1.000	1.000
	Test	0.665 (0.517-0.813)	0.623	0.867	0.526	0.419	0.909
LightGBM	Training	0.963 (0.936-0.989)	0.907	0.927	0.900	0.760	0.973
	Test	0.627 (0.479-0.776)	0.528	1.000	0.351	0.375	1.000
MLP	Training	0.829 (0.760-0.899)	0.770	0.780	0.767	0.533	0.911
	Test	0.686 (0.536-0.836)	0.604	0.867	0.514	0.406	0.905

LR: Logistic regression; SVM: Support vector machine; KNN: K-nearest neighbor; RF: Random forest; XGBoost: EXtreme gradient boosting; LightGBM: Light gradient boosting machine; ET: Extra trees; MLP: Multilayer perceptron; AUC: Area under the curve; CI: Confidence interval; ACC: Accuracy; SEN: Sensitivity; SPE: Specificity; PPV: Positive predictive value; NPV: Negative predictive value.