Research on coronary heart disease prediction model based on EasyEnsemble and XGBoost
In response to the issue of imbalanced medical samples,the integrated sampling EasyEnsemble algorithm and XGBoost algorithm are combined to build a coronary heart disease prediction model to improve the accuracy of disease sample recognition.Selecting the publicly available Framingham coronary heart disease dataset and after preprocessing the data,the EasyEnsemble algorithm is used to balance the dataset,and then the extreme gradient boosting algorithm XGBoost is used as the base classifier for training.Various experimental parameters are adjusted,and the model is evaluated using indicators such as accuracy,recall,and AUC(area under ROC curve).The experimental results show that compared to the three methods of XGBoost,oversampling SMOTE+XGBoost,and undersampling TomekLinks+XGBoost,the EasyEnsemble + XGBoost model greatly improves the recall rate.