医学信息2024，Vol.37Issue(11)：11-15,5.DOI:10.3969/j.issn.1006-1959.2024.11.002
三种机器学习算法预测心力衰竭死亡的价值研究
Value of Three Machine Learning Algorithms in Predicting Death from Heart Failure
摘要
Abstract
Objective To establish a classification and prediction model of heart failure by using three different algorithms of machine learning,compare the accuracy of the model,and analyze the importance characteristics of heart failure death events,so as to provide assistance for the early detection and implementation of intervention measures,and strive to improve people's health level and quality of life.Methods Using the heart failure data set published by Kaggle platform,the data preprocessing was carried out by missing value filling method,data standardization processing and SMOTE method.A heart failure prediction model was established based on random forest,C4.5 and AdaBoost algorithms.The performance evaluation index confusion matrix,ROC curve,root mean square error and mean absolute error were used to evaluate the performance of the model.Results In the order of importance of variables given by PermutationImportance,serum creatinine level,age and serum sodium level were ranked first.Among the three models,the accuracy of the random forest model was 85％,the accuracy was 81％,and the recall rate was 68％;the accuracy rate of the C4.5 model was 83％,the accuracy rate was 80％,and the recall rate was 63％.The accuracy rate of AdaBoost model was 80％,the accuracy rate was 71％,and the recall rate was 63％.Conclusion Based on the data set used,the random forest model is superior to the C4.5 model and the AdaBoost model.The heart failure death risk prediction model can provide a reference for early prevention,control and diagnosis of heart failure.关键词
心力衰竭/死亡/预测模型/C4.5/随机森林/AdaBoostKey words
Heart failure/Death/Prediction model/C4.5/Random forest/AdaBoost分类
医药卫生引用本文复制引用
陈晓彤,岑梓熹,谭静仪,栾雅,彭师师,严波,何震..三种机器学习算法预测心力衰竭死亡的价值研究[J].医学信息,2024,37(11):11-15,5.基金项目
1.校级大学生创新创业项目(编号:202213902120) （编号:202213902120）
2.校级科研项目(编号:2020KYQN03) （编号:2020KYQN03）
