Predicting gastric cancer survival using machine learning: A systematic review

Table 2 Classification of the features of the included articles

Characteristics	Categories	Number (n)
		OS	CSS	DFS
Dataset sources	Hospitals	6	-	3
	SEER	3	1	-
	TCGA	4	-	1
	NOGCA	1	-	-
	TANRIC	1	-	1
Dataset privacy	Public	8	1	1
	Private	6	-	3
Data source	Single	6	1	1
	Multiple	8	-	3
Preprocessing	Yes	14	1	3
	No	-	-	1
Feature selection	Yes	13	1	2
	No	1	-	2
Models	One	5	-	4
	Two or more	9	1	-
Models type	GB	1	-	-
	HGB	1	-	-
	KNN	1	-	-
	LR	2	-	-
	NB	1	-	-
	RF	6	-	-
	SVM	5	-	2
	XGboost	2	-	-
	DL	6	-	2
	MultiDeepCox-SC	1	-	-
	Ensemble learning	2	1	-
Validation	Internal	14	1	3
	External	8	-	2
Evaluation	C-index	10	-	3
	AUC	13	1	4
	Calibration	6	1	1
	Brier-score	4	-	1
	Accuracy	3	-	-
	Specificity	2	-	-
	Sensitivity	2	-	-
	F1-score	2	-	-
	IBS	1	-	-
Data types	Clinical	7	1	1
	Image	1	-	-
	Clinical + Image	1	-	2
	Clinical + Molecular	4	-	1
	Clinical + Molecular + Image	1	-	-

TCGA: The Cancer Genome Atlas; SEER: Surveillance, epidemiology, and end results; NOGCA: National oeosophago-gastric cancer audit; TANRIC: The atlas of noncoding RNAs in cancer; OS: Overall survival; CSS: Cancer-specific survival; DFS: Disease-free survival; RF: Random forest; SVM: Support vector machine; DL: Deep learning; KNN: K-nearest neighbors; NB: Naive bayes; GB: Gradient boosting; XGboost: Extreme gradient boosting; HGB: Histogram-based gradient boosting; LR: Logistic regression; AUC: Area under the curve; IBS: Integrated brier score.