Machine‐learning models
±
REGRESSION
Applying SVR (linear) directly to GPR did not result in good prediction values as some of the prediction values were greater than 100%, which is not acceptable for GPR.To avoid this issue, a logit transform was applied to GPRs. First, GPRs were divided by 100 to have data in the range between 0 and 1. However, as it is shown in Figure After applying the logit function, SVR was run on these real numbers and made predictions based on these numbers. Following this step, the numbers were transformed back to a percentage by using the inverse logit function (Equation 3).
For evaluation, both models were executed to a test set (36 treatment plans from different sites) that were not used during model development. For RFR, the accuracy was 0.91 with a standard deviation of 0.05, MAE = 1.14 ± 0.084, RMSE equals 1.407 ± 0.103, and the maximum error was 3.494% (Figure An example of a scatter plot of predicted gamma passing rate (GPR) against measured GPR (3%/2 mm) values for one of the train/test sets, (a) random forest regression, (b) support vector regression (linear)For both models, 2 out of 36 treatment plans had a percentage error greater than 3% indicating 94.44% of predictions within 3% of measured 3%/2 mm of GPR.The features that were chosen to build the model were ranked with their respective importance for both models (Figure Importance of each variable on gamma passing rate (GPR) prediction accuracy: (a) random forest regression (RFR), (b) support vector regression (SVR) (linear)