Table 6.

Specificities for sensitivities values provided in each column on validation data. The bold values reflect the best model for the sensitivity value presented in each of the columns.

Model	0.80	0.85	0.90	0.95
Logistic regression	0.779 (0.742,0.812)	0.724 (0.685,0.760)	0.620 (0.579,0.660)	0.507 (0.465,0.549)
Ridge regression	0.731 (0.693,0.767)	0.671 (0.631,0.710)	0.592 (0.550,0.632)	0.405 (0.364,0.446)
LASSO	0.751 (0.713,0.785)	0.689 (0.649,0.727)	0.613 (0.571,0.653)	0.392 (0.352,0.434)
Elastic net	0.749 (0.711,0.784)	0.691 (0.651,0.728)	0.611 (0.569,0.651)	0.387 (0.347,0.428)
Classification tree	0.717 (0.678,0.753)	0.635 (0.594,0.675)	0.490 (0.448,0.532)	0.245 (0.211,0.283)
Random forest	0.758 (0.720,0.792)	0.701 (0.661,0.738)	0.638 (0.596,0.677)	0.449 (0.407,0.491)
XGBoost	0.784 (0.748,0.817)	0.717 (0.678,0.754)	0.640 (0.598,0.679)	0.452 (0.411,0.494)
Neural network	0.793 (0.757,0.825)	0.740 (0.702,0.775)	0.634 (0.593,0.674)	0.486 (0.444,0.528)