From: Predicting hospitalization following psychiatric crisis care using machine learning
ML algorithm | AUC | Sensitivity | Specificity | Accuracy |
---|---|---|---|---|
Gradient boosting | 0.774 | 0.455 | 0.894 | 0.744 |
Oblique random forest | 0.762 | 0.509 | 0.847 | 0.732 |
DeepBoost | 0.760 | 0.461 | 0.871 | 0.731 |
Random forest | 0.757 | 0.478 | 0.864 | 0.732 |
GLM (logistic regression) | 0.756 | 0.444 | 0.876 | 0.729 |
Support vector machines | 0.751 | 0.370 | 0.917 | 0.731 |
Naive Bayes | 0.751 | 0.455 | 0.861 | 0.723 |
Neural network | 0.749 | 0.528 | 0.828 | 0.726 |
Keras/TensorFlow | 0.741 | 0.465 | 0.850 | 0.719 |
K-nearest neighbors | 0.702 | 0.356 | 0.879 | 0.701 |