Table 3 Evaluation of the prediction performance of five algorithms on the four datasets

Cancer	algorithms	sample_n	auc[CI]	tpr	spc	ppv	npv	f1	acc
BLCA	Stacking	70	0.93[0.87,0.98]	0.94	0.85	0.68	0.98	0.79	0.87
	RF	70	0.87[0.77,0.94]	0.94	0.71	0.53	0.97	0.68	0.77
	LR	70	0.79[0.63,0.92]	0.94	0.31	0.32	0.94	0.48	0.47
	SVC	70	0.83[0.70,0.93]	0.94	0.4	0.35	0.95	0.52	0.54
	XGBoost	70	0.96[0.91,0.99]	0.94	0.87	0.71	0.98	0.81	0.89
ccRCC	Stacking	84	0.99[0.97,1.00]	0.91	0.96	0.94	0.94	0.92	0.94
	RF	84	0.99[0.98,1.00]	0.91	0.98	0.97	0.94	0.94	0.95
	LR	84	0.97[0.93,1.00]	0.91	0.92	0.88	0.94	0.9	0.92
	SVC	84	0.95[0.89,0.99]	0.91	0.88	0.83	0.94	0.87	0.89
	XGBoost	84	0.98[0.96,1.00]	0.91	0.96	0.94	0.94	0.92	0.94
PRAD	Stacking	83	0.91[0.84,0.96]	0.92	0.77	0.65	0.96	0.76	0.82
	RF	83	0.89[0.81,0.95]	0.92	0.81	0.69	0.96	0.79	0.84
	LR	83	0.86[0.76,0.93]	0.92	0.77	0.65	0.96	0.76	0.82
	SVC	83	0.87[0.78,0.94]	0.92	0.77	0.65	0.96	0.76	0.82
	XGBoost	83	0.92[0.85,0.97]	0.92	0.81	0.69	0.96	0.79	0.84
Pan_Cancer	Stacking	152	0.88[0.82,0.93]	0.9	0.67	0.76	0.85	0.83	0.8
	RF	152	0.86[0.80,0.91]	0.9	0.56	0.7	0.83	0.79	0.74
	LR	152	0.82[0.75,0.88]	0.9	0.54	0.7	0.83	0.79	0.74
	SVC	152	0.83[0.76,0.89]	0.9	0.57	0.71	0.83	0.8	0.75
	XGBoost	152	0.89[0.83,0.94]	0.9	0.71	0.79	0.86	0.84	0.82

The highest value in each row is highlighted in bold and blue.
AUC area under the curve, TPR True Positive Rate, SPC Specificity, PPV Positive Predictive Value, NPV Negative Predictive Value, ACC Accuracy, F1 F1 Score.

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