Table 3 Performance of different classifiers to predict in-hospital mortality in the discovery cohort using the two selected features (age and LEF1-AS1)

From: Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality

Classifier

AUC (95% CI)

Accuracy (95% CI)

Sensitivity (95% CI)

Specificity (95% CI)

Brier score (95% CI)

RF

0.78 (0.76–0.80)

0.73 (0.71–0.75)

0.76 (0.73–0.78)

0.71 (0.68–0.74)

0.19 (0.18–0.20)

kNN

0.81 (0.79–0.83)

0.75 (0.73–0.77)

0.85 (0.83–0.87)

0.65 (0.62–0.68)

0.18 (0.17–0.19)

Logit

0.81 (0.79–0.83)

0.76 (0.74–0.77)

0.81 (0.78–0.84)

0.70 (0.67–0.73)

0.18 (0.17–0.19)

MLP

0.82 (0.80–0.84)

0.77 (0.75–0.79)

0.82 (0.80–0.84)

0.72 (0.69–0.75)

0.18 (0.17–0.18)

SVM

0.67 (0.62–0.72)

0.74 (0.72–0.76)

0.82 (0.80–0.84)

0.67 (0.63–0.70)

0.21 (0.20–0.22)

XGB

0.74 (0.72–0.76)

0.68 (0.66–0.70)

0.69 (0.66–0.71)

0.67 (0.64–0.70)

0.25 (0.24–0.26)

Back to article page