Prediction of Heparin Dose during Continuous Renal Replacement Therapy Surgery by Using the Gradient Boosting Regression Model

摘要

In order to reduce the doctor's subjective misjudgments, it is necessary to explore an effective predictive model to predict heparin dose during continuous renal replacement therapy surgery. In this paper, we use a combination of random forest and genetic algorithm to extract features and use EasyEnsemble algorithm to deal with unbalanced data. When training the model, this paper takes the ln transformation of the targeted variables, and then uses the Gradient Boosting Regression model and the Decision Tree Regression model to train. By comparing their mean absolute error, mean square error and square of R, finally, this paper chooses the Gradient Boosting Regression model as the final predictive model. The purpose of this study is to assist doctors in making more accurate judgments by this predictive model.