. 2026 Jan 8;10:1. doi: 10.1186/s41512-025-00216-5

Table 2.

Performance measures

Model	Performance measures
Unpenalized logistic regression	0.84 (0.83-0.85)	0.98 (0.98-0.99)	0.31 (0.30-0.33)	0.25 (0.24-0.26)	0.98 (0.98-0.99)
Ridge logistic regression	0.84 (0.83-0.85)	1.00	0	0.19 (0.18-0.19)	NaN
Random Forest	0.81 (0.80-0.82)	0.81 (0.79-0.83)	0.81 (0.80-0.82)	0.50 (0.47-0.52)	0.94 (0.94-0.95)
Support Vector machine	0.82 (0.81-0.83)	1.00	0	0.19 (0.18-0.19)	NaN
Neural Network	0.84 (0.83-0.85)	0.82 (0.79-0.84)	0.75 (0.73-0.76)	0.43 (0.41-0.45)	0.95 (0.94-0.95)

Model

Performance measures

AUC
(95% CI)

Sensitivity
(95% CI)

Specificity
(95% CI)

PPV
(95% CI)

NPV
(95% CI)

Unpenalized logistic regression

0.84

(0.83-0.85)

0.98

(0.98-0.99)

0.31

(0.30-0.33)

0.25

(0.24-0.26)

0.98

(0.98-0.99)

Ridge logistic regression

0.84

(0.83-0.85)

1.00

0.19

(0.18-0.19)

NaN

Random Forest

0.81

(0.80-0.82)

0.81

(0.79-0.83)

0.81

(0.80-0.82)

0.50

(0.47-0.52)

0.94

(0.94-0.95)

Support Vector machine

0.82

(0.81-0.83)

1.00

0.19

(0.18-0.19)

NaN

Neural Network

0.84

(0.83-0.85)

0.82

(0.79-0.84)

0.75

(0.73-0.76)

0.43

(0.41-0.45)

0.95

(0.94-0.95)

CI Confidence interval. The 95% confidence intervals for AUC were calculated using the DeLong method and for classification measures using the Wilson score interval. PPV Positive predictive value. NPV Negative predictive value. NaN Not a Number