Performance and Validation of the Prediction Model
All models were evaluated using the test data. The metrics used to assess performance include Area Under the Curve (AUC), accuracy, precision, and recall in accordance with previous recommendations for results reporting of clinical prediction models.14 The model with the highest performance in the most performance metrics was chosen as the final model. The permutation feature importance (PFI) scores were also obtained to illuminate the most significant variables used in the model’s prediction. The PFI scores are the difference in model performance determined by the AUC before and after alteration of a given dependent variable. Thus, the absolute magnitude of a PFI score reflects the impact an individual variable has on the overall performance.