论文标题
使用Rapid -Mininer GO（Java）的人工智能算法评估和预测立体定向放射外科计划的效率指数
Evaluating and predicting the Efficiency Index for Stereotactic Radiosurgery Plans using RapidMiner GO(JAVA) Based Artificial Intelligence Algorithms
论文作者
论文摘要
研究了使用监督的机器学习对SRS治疗计划的效率指数的预测，并且研究了Rapidminer的预测模型算法的性能在参数预测中。 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）。均方根误差（RMSE），平均绝对误差，绝对相对误差，平方相关性和模型构建时间以评估每种算法的性能。根据结果，GLR算法模型的平方相关性为0.974，最小的RMSE为0.01，相对较高的预测速度和2.812 s的快速构建时间。所有模型的RMSE值介于0.01之间，最高为0.021，所有算法的性能都很好。发现梯度增强的树，随机森林和决策树回归算法的RMSE大于0.01，这表明它们在此分析中不适合预测EI。 RapidMiner GO机器学习模型可用于预测SRS治疗计划中的EI等DVH参数QA。为了有效评估参数，有必要选择合适的机器学习算法。
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.
