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Table 3 Key performance statistics of the trained models

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
  1. The base rate of (non-)hospitalization = 0.659. The accuracy of each model was tested against this base rate, all p < 0.00001, based on 2-sided z-tests; hence each model led to a significant improvement in classification accuracy compared to an intercept only model
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