吉林大学学报（理学版）2025，Vol.63Issue(2)：472-478,7.DOI:10.13413/j.cnki.jdxblxb.2024223
基于Bayes超参数优化梯度提升树的心脏病预测方法
Heart Disease Prediction Method Based on Bayesian Hyperparameter Optimization Gradient Boosting Trees
摘要
Abstract
Aiming at the problem of low prediction accuracy of traditional machine learning algorithms on Cleveland and Hungary dataset,we proposed a heart disease prediction method based on Bayesian hyperparameter optimization gradient boosting trees.Firstly,the K-nearest neighbor algorithm was used to fill in the missing values in the dataset,Min-Max standardization and One-Hot encoding were used to process the data,and the gradient boosting tree algorithm was used to predict the heart disease.Secondly,Bayesian optimization and ten-fold cross validation were used to search for the best combination of hyperparameters of the algorithm.The experimental results show that the prediction accuracy of the optimized gradient boosting tree algorithm can reach 90.2%on the Cleveland heart disease dataset,and the prediction accuracy can reach 81.4%on the Hungarian heart disease dataset,outperforming traditional machine learning methods such as decision tree,support vector machine and the K-nearest neighbor,it can assist doctors in the diagnosis of heart disease.关键词
心脏病预测/K-最近邻算法/梯度提升树/Bayes优化Key words
heart disease prediction/K-nearest neighbor algorithm/gradient boosting tree/Bayesian optimization分类
信息技术与安全科学引用本文复制引用
王海燕,焦增晨,赵剑,安天博,鞠熠..基于Bayes超参数优化梯度提升树的心脏病预测方法[J].吉林大学学报（理学版）,2025,63(2):472-478,7.基金项目
吉林省教育厅科学技术研究项目(批准号:JJKH20220597KJ)和吉林省科技发展计划项目(批准号:YDZJ202201ZYTS549). （批准号:JJKH20220597KJ）
