Fig. 2
From: Machine learning methods to identify risk factors for corneal graft rejection in keratoconus
Variable importance plot showing variables for predicting graft rejection after corneal transplantation for keratoconus using artificial neural network. Predictors with variable importance above 0.00 contribute to the prediction accuracy of the model. The factor with the most discriminative power in this model was time from keratoplasty to complete suture removal, graft quality, technique of corneal transplantation, and duration of corticosteroid application in descending order.
