Table 2.
Mean Accuracy Using Train/Validation Dataset (Cohort A).
| Mean (SD) Accuracy (%) Biomarker Combination | DNN | LR | k-NN | SVM | RF |
|---|---|---|---|---|---|
| NGAL, NT-proBNP, UOP, Creatinine | 100 (0) | 95 (10) | 95 (10) | 98 (8) | 90 (17) |
| NGAL, UOP, NT-proBNP | 88 (17) | 88 (17) | 90 (17) | 83 (23) | 90 (12) |
| NGAL, UOP, Creatinine | 100 (0) | 98 (8) | 98 (8) | 98 (8) | 93 (16) |
| NGAL, NT-proBNP, Creatinine | 98 (8) | 95 (10) | 95 (10) | 95 (10) | 93 (11) |
| NT-proBNP, Creatinine, UOP | 90 (17) | 88 (17) | 93 (16) | 93 (16) | 93 (11) |
| NGAL, NT-proBNP | 93 (11) | 93 (11) | 93 (11) | 90 (17) | 90 (17) |
| NGAL, Creatinine | 95 (10) | 95 (10) | 95 (10) | 95 (10) | 93 (16) |
| NGAL, UOP | 90 (17) | 83 (22) | 90 (17) | 88 (17) | 90 (17) |
| NT-proBNP, Creatinine | 90 (12) | 88 (13) | 88 (13) | 90 (12) | 90 (12) |
| NT-proBNP, UOP | 85 (20) | 85 (20) | 78 (21) | 85 (20) | 90 (12) |
| Creatinine, UOP | 65 (20) | 48 (18) | 65 (20) | 60 (20) | 60 (23) |
| NGAL | 85 (17) | 83 (16) | 85 (17) | 85 (17) | 85 (17) |
| Creatinine | 68 (16) | 58 (39) | 65 (32) | 68 (20) | 65 (20) |
| UOP | 58 (16) | 30 (19) | 48 (13) | 43 (20) | 50 (25) |
Note: The number of neighbors for k-NN ranted from 1 to 30 for the grid search process on both uniform and distance weight measures. An optimal k-value of 14 was identified within the Minkowski Metric. For RF, 1350 models were generated through the grid search process with multiple random hyperparameter settings. The best performing RF model was comprised of 100 trees (n-estimator = 100) with a maximum depth 3.
Abbreviations: DNN, deep neural network; k-NN, k-nearest neighbor; LR, logistic regression; NGAL, neutrophil gelatinase associated lipocalin; NT-proBNP; N-terminal pro-B-type-natriuretic peptide; RF, random forest; SVM, support vector machine; and UOP, urine output.