Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors

Table 2 Different machine learning algorithms were used to predict the patient’s overall survival and progression-free survival performance

Indicator	OS						PFS
	XGB1	LR1	DT1	XGB2	LR2	DT2	XGB1	LR1	DT1	XGB2	LR2	DT2
AUC	0.732	0.652	0.758	0.695	0.628	0.608	0.835	0.581	0.667	0.677	0.511	0.696
Accuracy	0.712	0.613	0.723	0.634	0.573	0.610	0.759	0.529	0.607	0.565	0.524	0.549
Precision	0.723	0.692	0.713	0.621	0.523	0.612	0.759	0.264	0.735	0.585	0.262	0.583
Recall ratio	0.671	0.540	0.708	0.619	0.511	0.615	0.757	0.500	0.585	0.586	0.500	0.529
F1 score	0.673	0.485	0.710	0.620	0.464	0.608	0.758	0.346	0.519	0.585	0.344	0.448
PPV	0.745	0.611	0.524	0.563	0.455	0.524	0.756	0	0.895	0.561	0	0.625
NPV	0.701	0.613	0.700	0.680	0.592	0.700	0.762	0.529	0.576	0.610	0.524	0.541

¹Training set.

²Test set.

OS: Overall survival; PFS: Progression-free survival; XGB: eXtreme Gradient Boosting; LR: Logistic regression; DT: Decision tree; AUC: Area under the receiver operating characteristic curve; PPV: Positive predictive value; NPV: Negative predictive value.