The Prediction Model of Survival Activity in Lung Cancer Patients Based on Stacking Integrated Learning
In order to improve the accuracy of survivability prediction for lung cancer patients,a survivability prediction model for lung cancer patients based on Stacking integrated learning is proposed.Firstly,the data set is preprocessed,feature selection,variable conversion,etc.Furthermore,XGBoost(eXtreme Gradient Boosting),SVM(Support Vector Machine)and LR(Logistic Regression)algorithms were used as the based learning algorithms and naive Bayes as the meta-learning algorithms to construct the model.Secondly,the Grid Search method was used to optimize the hyperparameters and cross validation method to conduct simulation experiments on the lung cancer data set disclosed by SEER.The results show that the prediction accuracy of this model is 85％,which is 10％higher than that of the single model.Therefore,this model has better accuracy and interpretation in the prediction of survival activity of lung cancer patients,and can well provide decision support for the prognosis of lung cancer patients to make up for the lack of experience.
survival predictionlung cancer patientintegrated learningcross validation
万方数据
维普
