Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy - Fig 1

Results of the recursive feature elimination algorithm applied to 6-month follow-up (A), 12-month follow-up (B), and 24-month follow-up (C). The figures demonstrate the change in root mean squared error (RMSE) as features were iteratively added to the model. As greater number of features were added to the model the RMSE decreased to a minimum value, demonstrating best model fit, then began to increase as greater numbers of ‘distracting’ features were added. The set of features that achieved the minimum RMSE were used for model training (shown by vertical black line).