Table 3.

Comparison of prediction performance of different classification models

	Logistic regression			LightGBM
	1-year	2-year	3-year	1-year	2-year	3-year
AUC	0.687 (0.680,0.694)	0.667 (0.660, 0.673)	0.683 (0.677, 0.689)	0.718* (0.710, 0.727)	0.744* (0.737, 0.751)	0.757* (0.751, 0.763)
Accuracy	0.709 (0.705, 0.713)	0.715 (0.710, 0.720)	0.732 (0.727, 0.737)	0.853 (0.850, 0.857)	0.855 (0.852, 0.859)	0.864 (0.861, 0.868)
Sensitivity	0.662 (0.646, 0.677)	0.606 (0.593, 0.619)	0.618 (0.605, 0.631)	0.559 (0.542, 0.576)	0.603 (0.589, 0.617)	0.615 (0.603, 0.626)
Specificity	0.713 (0.708, 0.717)	0.727 (0.722, 0.734)	0.748 (0.742, 0.754)	0.878 (0..875, 0.881)	0.885 (0.882, 0.888)	0.900 (0.897, 0.903)
f-measure	0.257 (0.250, 0.264)	0.308 (0.300, 0.315)	0.363 (0.355, 0.371)	0.367 (0.356, 0.378)	0.465 (0.455,0.475)	0.528 (0.519, 0.537)
Brier score	0.291 (0.287, 0.295)	0.285 (0.280, 0.290)	0.268 (0.263,0.273)	0.146 (0.143, 0.150)	0.145 (0.141, 0.148)	0.135 (0.132, 0.138)

Data are presented as mean (95% confidence interval), CI: confidence interval, AUC: area under the curve

^*DeLong test, P < 0.05, the AUC of 1-, 2-, and 3-year all-cause mortality were different between LightGBM and logistic regression model