Table 1 Summary of all published models of scheduled appointment attendance in healthcare—ranked by area under the receiver operating characteristic curve in order of performance—for which out-of-sample metrics are available
From: Predicting scheduled hospital attendance with artificial intelligence
Model | Type | Variable count | Predictive performance (AUC) |
|---|---|---|---|
Stacking17 | Non-linear | 18 | 0.846 |
XGBoost5 | Non-linear | 42 | 0.834 |
Neural network6 | Non-linear | Not available | 0.81 |
Logistic regression16 | Linear | 38 | 0.75 |
Logistic regression7 | Linear | 49 | 0.713 |
Logistic regression17 | Linear | 14 | 0.706 |
Sums of exponentials for regression8 | Linear | 17 | 0.706 |
Logistic regression9 | Linear | 13 | 0.702 |
