Table 8.
Precision and recall when adjusting the prediction points/ upper bounds of data collection window
| Data Collection Window | Classification Models | ||
|---|---|---|---|
| Random Forest(Precision/Recall) | AdaBoostM1 (Precision/Recall) | Logistic (Precision/Recall) | |
| [Admission_date, AKI_date-1] | 0.692 / 0.711 | 0.662 / 0.736 | 0.704 / 0.711 |
| [Admission_date, AKI_date-2] | 0.675 / 0.661 | 0.643 / 0.714 | 0.678 / 0.675 |
| [Admission_date, AKI_date-3] | 0.650 / 0.650 | 0.625 / 0.687 | 0.651 / 0.646 |
| [Admission_date, AKI_date-4] | 0.634 / 0.637 | 0.628 / 0.656 | 0.620 / 0.623 |
| [Admission_date, AKI_date-5] | 0.623 / 0.610 | 0.616 / 0.674 | 0.608 / 0.605 |