Development of a machine learning-based model for predicting postoperative survival in gastric cancer

Table 4 Comparative analysis of the ten machine learning models

Algorithms	Accuracy	Sensitivity	Precision	Specificity	F1 score
SVM	0.587	0.476	0.556	0.680	0.513
XGBoost	0.696	0.571	0.706	0.700	0.632
LightGBM	0.750	0.738	0.706	0.745	0.729
LR	0.717	0.714	0.682	0.720	0.698
RF	0.707	0.691	0.674	0.720	0.682
MLP	0.663	0.786	0.600	0.560	0.680
DT	0.739	0.810	0.680	0.680	0.739
GB	0.674	0.476	0.714	0.715	0.571
KNN	0.609	0.452	0.594	0.740	0.514
ET	0.772	0.857	0.721	0.760	0.774

SVM: Support vector machine; XGBoost: Extreme gradient boosting; LightGBM: Light gradient boosting machine; LR: Logistic regression; RF: Random forest; MLP: Multi-layer perceptron; DT: Decision tree; GB: Gradient boosting; KNN: K-nearest neighbors; ET: Extremely randomized trees.