Model characteristics including default variable importance of random forest models used to determine individual feature contributions.

The feature’s importance is calculated by removing the feature of interest from the combined model, retrain, evaluate the model by correlating measured and predicted postprandial glucose responses and subsequently subtract the correlation R from the one obtained from the combined model including all of the features. Hyperparameter names in the models correspond to parameter names from the h2o package in R. Training and test evaluations as the mean squared error, root mean squared error, mean absolute error, root mean squared logarithmic error, mean of the residuals, correlation between predicted and measured postprandial plasma glucose concentrations including confidence intervals and P values, respectively, are presented. (XLSX)