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基于支持向量机的冠心病辅助诊断研究 被引量：2

Studies on appilcation of Support Vector Machine in detection of coronary heart disease
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摘要 支持向量机(SVM)是在统计学习理论基础上发展而来的一种新的通用学习方法,较好地解决了有限样本的学习分类问题。用支持向量机的分类算法,选取不同的核函数,构造了支持向量机的不同分类器,并将其应用于冠心病的预测诊断。仿真结果表明,非线性的支持向量机取得了较高的准确率,支持向量机在早期冠心病的诊断中有很大的应用潜力。 Support Vector Machine（SVM） is an efficient method originated from the statistical learning theory.It is powerful in machine learning to solve problems with finite samples.SVM is employed in detecting coronary heart disease and the results are encouraged compared with conventional methods.The accuracy of non-linear SVM classifier is especially high in all kinds of classifiers,which indicates the potential application of SVM in coronary heart disease detection.
作者 吕晓燕 罗立民 李祥生 郭建军
机构地区 山西医科大学计算中心 东南大学计算机科学与工程系 山西医科大学第一医院
出处 《计算机工程与应用》 CSCD 北大核心 2008年第36期221-223,229,共4页 Computer Engineering and Applications
基金 山西省“十一五”规划课题(No.GH-06211) 山西医科大学青年基金资助项目
关键词 支持向量机 核函数 模式识别 冠心病 Support Vector Machine（SVM） kernel function pattern recognition coronary heart disease
分类号 TP391.9 [自动化与计算机技术—计算机应用技术]
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参考文献15

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二级参考文献15

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共引文献53

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计算机工程与应用
2008年 第36期

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