  Evaluation the prediction of Efficiency index by DVH parameter for SRS
treatment plans using Supervised Machine learning and the performance of
predictive model algorithms of RapidMiner GO in the parameter prediction are
investigated. Dose volume histogram (DVH) based Efficiency index was calculated
for 100 clinical SRS plans generated by Leksell Gamma plan, and the results
were compared to predicted values produced by machine learning toolbox of
RapidMiner Go, algorithms are namely, Generalized linear model (GLR), Decision
Tree Model, Support Vector Machine (SVM), Gradient Boosted Trees (GBT), Random
Forest (RF) and Deep learning Model (DL). Root mean square error (RMSE),
Average absolute error, Absolute relative error, squared correlation and model
building time were determined to evaluate the performance of each algorithm.
The GLR algorithm model had square correlation of 0.974 with the smallest RMSE
of 0.01, relatively high prediction speed, and fast model building time with
2.812 s, according to the results. The RMSE values for all models were between
0.01 upto 0.021, all algorithms performed well. The RMSE of the Gradient
Boosted Tree, Random Forest, and Decision Tree regression algorithms was found
to be greater than 0.01, suggesting that they are not appropriate for
predicting EI in this analysis. RapidMiner GO machine learning models can be
used to predict DVH parameters like EI in SRS treatment planning QA. To
effectively evaluate the parameter, it is necessary to choose a suitable
machine learning algorithm.
