Physiological measures
confusion
Spirometry and two MSWT were collected using standard protocols.Models were programmed using R V.3.6.1 (2019-07-05). Modelling procedure remained the same for each of the three models (A schematic of the modelling procedure adapted from an illustration by Chekroud and colleagues.All measures were centred and scaled. Checks were performed to determine whether any measures were highly correlated (R>0.8) or linear combinations of each other.To correct for imbalance in the number of responders/non-responder a resampling procedure. Imbalanced classes can affect classifier performance. Random OverSample Examples (ROSE) was carried out. ROSE, an R package, creates an artificially balanced sample using a smoothed bootstrap approach.An elastic net procedure was used to identify the number most relevant features for inclusion into the model. Elastic net procedure was selected for its ability to regularise, improve data sparsity via feature selection and cluster correlated measures together (for more details see Model training parameters—C, kappa and sigma were selected based on an internal repeated cross-validation procedure (10-fold cross validation repeated 3 times). In all instances automated tuning parameter selection for the values, with a tune length of 5, was used within R’s caret package. Train test data were kept separate across folds, with the algorithm never having access to the entire dataset. The best tuning parameters were selected automatically by R’s caret package from across cross validation folds.These parameters were used to train a Support Vector Classifier with radial kernel to predict outcomes in the entire dataset.Model performance was assessed internally using accuracy, sensitivity, specificity and area under the curve. Full confusion matrices are presented along with calibration curves. Model significance was assessed with a one-tailed binomial test of model accuracy compared with the null information rate.