摘要
研究冠心病诊断问题,冠心病影响指标多,指标体系中存在大量的重复信息,传统方法无法消除重复信息,冠心病诊断准确率低,误诊率高。为了提高冠心病诊断准确率,提出一种智能的冠心病诊断算法。首先科学、准确地选择冠心病指标体系,然后采用主成分分析对指标体系进行筛选,消除重复信息,最后采用支持向量机对冠心病建立分类器,实现冠心病智能诊断。仿真结果表明,相对于其它传统冠心病诊断方法,智能诊断算法有效提高了冠心病诊断准确率,克服了传统诊断缺限,为冠心病临床诊断提供了依据。
In order to improve the diagnosis accuracy of coronary heart disease, this paper proposed a new method based on the support vector machine algorithm. Firstly, selecting coronary heart disease index system scientifically and accurately, and then the principal components were analyzed in index system for screening. Finally, by using support vector machine, the classifiers of coronary heart disease were built to realize intelligent coronary heart disease diagnosis. The simulation resuhs show that, compared with traditional coronary heart disease diagnosis method, the proposed method improves the diagnosis accuracy of coronary heart disease and overcomes the traditional diagnostic limitations. It can meet the practical needs of clinical diagnoses of coronary heart diseases.
出处
《计算机仿真》
CSCD
北大核心
2012年第12期203-206,共4页
Computer Simulation
基金
国家自然科学基金项目(61175027)
关键词
冠心病
智能诊断
支持向量机
主成分分析
Coronary heart disease
Intelligent diagnosis
Support vector machine（SVM）
Principal component analysis （PCA）
