[1]王成武,郭志恒,晏峻峰*.改进的支持向量机在心脏病预测中的研究[J].计算机技术与发展,2022,32(03):175-179.[doi:10. 3969 / j. issn. 1673-629X. 2022. 03. 029]
　WANG Cheng-wu,GUO Zhi-heng,YAN Jun-feng*.Application of Improved Support Vector Machine in Heart Disease Prediction[J].,2022,32(03):175-179.[doi:10. 3969 / j. issn. 1673-629X. 2022. 03. 029]
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改进的支持向量机在心脏病预测中的研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年03期
页码:
175-179
栏目:
应用前沿与综合
出版日期:
2022-03-10

文章信息/Info

Title:
Application of Improved Support Vector Machine in Heart Disease Prediction
文章编号:
1673-629X(2022)03-0175-05
作者:
王成武; 郭志恒; 晏峻峰*
湖南中医药大学 信息科学与工程学院,湖南 长沙 410208
Author(s):
WANG Cheng-wu; GUO Zhi-heng; YAN Jun-feng*
School of Information Science and Engineering,Hunan University of Chinese Medicine,Changsha 410208,China
关键词:
心脏病; 支持向量机; 网格搜索; 粒子群优化; 灵敏度; 特异度
Keywords:
heart disease; support vector machine; grid search; particle swarm optimization; sensitivity; specificity
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 03. 029
摘要:
应用支持向量机对心脏病患者和非心脏病患者的分类进行研究,构建心脏病预测模型,辅助医生进行心脏病诊断。 选用径向基核函数构造支持向量机分类器,利用网格搜索与交叉验证相结合的方法对模型进行初步的优化,缩小参数寻优的取值范围,在此基础上使用粒子群优化算法( PSO) 对模型进行进一步优化,得到模型最佳的惩罚因子 C 和核参数 g 。 将优化前的支持向量机和参数优化后的支持向量机预测的结果进行比较,可看出优化后模型分类预测的结果得到了明显的提升,分类准确率提升到 84. 04% ,灵敏度和特异度分别提升到 92. 73% 和 71. 79% 。 通过对实验结果的观察,可看出该心脏病预测模型的分类准确率得到了提升,可应用于心脏病辅助诊断。
Abstract:
The classification of heart disease patients and non-heart disease patients is studied by using support vector machine, and the prediction model of heart disease is constructed to assist doctors in the diagnosis of heart disease. The radial basis kernel function is selected to construct the support vector machine classifier,and the grid search and cross-validation are combined to preliminarily optimize the model and reduce the range of parameter optimization. On this basis, the particle swarm optimization ( PSO) is used to further optimize the model,and the optimal penalty parameter C and kernel parameter g of the model are determined. By comparing the prediction results of the support vector machine before optimization and the support vector machine after parameter optimization,it can be seen that the classification prediction results of the optimized model have been significantly improved,with the classification accuracy increased to 84. 04% ,sensitivity and specificity increased to 92. 73% and 71. 79% ,respectively. Through the observation of the experimental results,it can be seen that the classification accuracy of the heart disease prediction model has been improved,which can be applied to assisted diagnosis of heart disease.

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更新日期/Last Update: 2022-03-10