云南民族大学学报（自然科学版）2025，Vol.34Issue(1)：77-83,7.DOI:10.3969/j.issn.1672-8513.2025.01.010
基于改进SVM的心音分类研究
Research on heart sound classification based on improved support vector machine
摘要
Abstract
Cardiovascular disease has always been a major factor threatening human life and health.If the patho-logical information contained in human heart sound signals can be accurately classified,it will be very helpful for disease diagnosis and control.Firstly,particle swarm optimization algorithm is used to optimize the traditional sup-port vector machine algorithm,and a binary classifier model is proposed.The primary classifier is composed of three algorithms Adaboost,RF and PSOA-SVM based on Stacking method,and the secondary classifier is LR model;Secondly,the improved Grey Wolf Optimization Algorithm is used to find the optimal parameter combina-tion of support vector machine to get a new classifier model;Finally,the heart sound data set is used to analyze the two classifier models.The experiments show that the two models show excellent classification results.关键词
支持向量机/PSO/GWO/Stacking/心音分类Key words
support vector machine/PSO/GWO/stacking/heart sound classification分类
信息技术与安全科学引用本文复制引用
殷丽凤,赵敏..基于改进SVM的心音分类研究[J].云南民族大学学报（自然科学版）,2025,34(1):77-83,7.基金项目
国家自然科学基金(61771087). （61771087）
