Table 2 Area under ROC curves and 95 % confidence intervals obtained using different machine learning methods for predicting and detecting AKI during hospital stay. In each column, the highest ROC value is shown in bold and the values found statistically significantly different (p < 0.05; two-tailed paired t-test) from it are indicated with a asymbol
From: Prediction and detection models for acute kidney injury in hospitalized older adults
Method | Prediction at 24 h (95 % CI) | Detection (95 % CI) |
|---|---|---|
Logistic Regression | 0.660 (0.647–0.673) | 0.743 (0.732–0.755) |
Naïve Bayes | a0.654 (0.639–0.669) | a0.699 (0.684–0.715) |
Decision Trees | a0.639 (0.627–0.651) | a0.725 (0.717–0.733) |
Support Vector Machine | a0.621 (0.609–0.633) | a0.692 (0.682–0.702) |
Ensemble | 0.664 (0.651–0.676) | a0.738 (0.727–0.748) |