Table 3 Bias, precision and accuracy of each model in the external validation data set
From: Improving precision of glomerular filtration rate estimating model by ensemble learning
Variable | Measured GFR (ml/min/1.73 m2) | |||
|---|---|---|---|---|
Overall | < 30 | ≥ 30 and < 60 | ≥ 60 | |
Bias = median difference (95% CI) | ||||
Regression model | 2.3 (1.0–3.4) | 4.4 (2.9–5.9) | 3.1 (1.5–6.5) | −1.9 (−4.5 to 0.9) |
ANN model | 3.2 (2.2–5.4) | 5.4 (3.1–7.4) | 5.4 (2.4–7.6) | 0.8 (−3.9 to 2.7) |
SVM model | 3.6 (2.6–4.9) | 6.8 (4.9–9.0)‡ | 4.0 (2.2–6.4) | −0.2 (−2.3 to 2.6) |
Ensemble model | 3.4 (2.3–4.4) | 5.6 (3.7–8.2) | 4.0 (2.1–6.7) | −0.5 (−3.9 to 2.3) |
Precision = IQR of the difference (95% CI) | ||||
Regression model | 14.0 (12.4–15.9) | 9.2 (7.3–11.8) | 13.5 (11.2–18.0) | 19.6 (16.8 to 23.5) |
ANN model | 15.1 (13.6–17.0)‡ | 11.1 (9.1–14.8)‡ | 14.9 (13.1–17.7)‡ | 20.5 (17.9 to 25.1)‡ |
SVM model | 14.2 (12.4–16.0)‡ | 9.5 (7.5–12.1)‡ | 12.9 (10.3–16.2)‡ | 18.5 (14.9 to 21.5)‡ |
Ensemble model | 13.5 (11.8–14.9)‡ | 8.9 (7.0–11.0)‡ | 12.7 (10.4–16.0)‡ | 17.9 (15.44 to 21.9)‡ |
Accuracy = 30% accuracy (95% CI) | ||||
Regression model | 75.1 (70.7–79.4) | 52.4 (42.7–61.2) | 75.2 (67.8–81.9) | 89.1 (83.6 to 93.3) |
ANN model | 73.4 (69.0–77.2) | 54.4 (44.7–64.1) | 70.5 (63.11–77.2) | 87.9 (82.4 to 92.1) |
SVM model | 73.1 (68.8–77.2) | 47.6 (37.9–57.3) | 71.1 (63.11–77.9) | 90.9 (86.1 to 94.5) |
Ensemble model | 75.5 (71.5–79.6) | 52.4 (42.7–62.1) | 73.8 (65.11–79.9) | 91.5 (86.7 to 95.2) |