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- W3147187979 abstract "The Machine Learning method Random Forests Regression is used to predict the Gamma index passing rate percentage of radiosurgery therapy plans. A software application that evaluates the output data is developed by the aid of the scikit-learn library of the Python programming language. The regression model contains four independent variables that are parameters of the plans - monitor units, normalization point, dose per fraction and planning target volume. The dependent variable is the Gamma passing rate that has to be predicted. It was found that the planning target volume is the variable with the biggest importance, and that monitor units is the variable which is second by importance. The model predicts the Gamma passing rate with an average accuracy greater than 97%. The accuracy of prediction increases when the planning target volumes are bigger." @default.
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- W3147187979 date "2021-01-01" @default.
- W3147187979 modified "2023-10-16" @default.
- W3147187979 title "Dose distribution prediction of Gamma index using Random Forests Regression. A retrospective comparison" @default.
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- W3147187979 doi "https://doi.org/10.1063/5.0047862" @default.
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