Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer

Table 4 Comparison of clinical models based on various machine learning methods

Clinical model		Accuracy	AUC	95%CI	Sensitivity	Specificity	PPV	NPV
LR	Train	0.81	0.837	0.6807-0.9938	0.636	0.871	0.636	0.871
	Test	0.278	0.482	0.1362-0.8281	0.75	0.143	0.2	0.667
SVM	Train	0.238	0.151	0.0000-0.3234	0.909	0	0.244	0
	Test	0.444	0.571	0.2439-0.8990	0.75	0.357	0.25	0.833
KNN	Train	0.786	0.787	0.6543-0.9205	0.182	1	1	0.775
	Test	0.722	0.607	0.3612-0.8531	0.25	0.857	0.333	0.8
RF	Train	0.929	0.993	0.9763-1.0000	0.818	0.968	0.9	0.937
	Test	0.556	0.688	0.4253-0.9497	0.75	0.5	0.3	0.875
Extra trees	Train	0.976	0.999	0.9945-1.0000	0.909	1	1	0.969
	Test	0.611	0.589	0.2299-0.9487	0.5	0.643	0.286	0.818
XGBoost	Train	0.857	0.937	0.8683-1.0000	0.818	0.871	0.692	0.931
	Test	0.667	0.696	0.4403-0.9526	0.5	0.714	0.333	0.833
MLP	Train	0.833	0.82	0.6535-0.9858	0.727	0.871	0.667	0.9
	Test	0.722	0.429	0.0520-0.8052	0	0.929	0	0.765

NPV: Negative predictive value; PPV: Positive predictive value; CI: Confidence interval; AUC: Area under the curve; LR: Logistic regression; KNN: K-nearest neighbor classification; RF: Random forest; XGBoost: EXtreme gradient boosting; MLP: Multilayer perceptron; SVM: Support vector machine.