摘要
本文基于体检获得的血压、血脂、尿糖和尿酸等数据指标,应用支持向量机(SVM)对南方人群冠心病患者和非冠心病患者进行分类研究,为进一步的预防和治疗提供指导。首先选取径向基核函数、线性核函数和多项式核函数,构造了SVM分类器,再采用粒子群优化(PSO)算法SVM惩罚参数C和核参数σ,最后进行冠心病的诊断和预测。通过与反向传播模型的人工神经网络、线性判别分析法、Logistic回归分析及优化前的SVM进行比较,我们的计算结果显示优化后的RBF-SVM的总体分类效果要优于其他数据挖掘算法,其分类准确率、敏感性和特异性分别高达94.51%、92.31%及96.67%。研究表明SVM在心血管疾病的预测和辅助诊断中有很大的应用潜力。
Base on the data of blood pressure, plasma lipid, Glu and UA by physical test, Support Vector Machine （SVM） was applied to identify coronary heart disease （CHD） in patients and non-CHD individuals in south China population for guide of further prevention and treatment of the disease. Firstly, the SVM classifier was built using radial basis kernel function, liner kernel function and polynomial kernel function, respectively. Secondly, the SVM penalty factor C and kernel parameter ~ were optimized by particle swarm optimization （PSO） and then employed to diagnose and predict the CHD. By comparison with those from artificial neural network with the back propagation （BP） model, linear discriminant analysis, logistic regression method and non-optimized SVM, the overall results of our calculation demonstrated that the classification performance of optimized RBF-SVM model could be superior to other classifier algorithm with higher accuracy rate, sensitivity and specificity, which were 94.51%, 92.31% and 96.67%respectively. So, it is well concluded that SVM could be used as a valid method for assisting diagnosis of CHD.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2013年第6期1180-1185,共6页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(10972081
11072080)
广东省自然科学基金资助项目(S2011010005451)
关键词
支持向量机
冠心病
分类预测
粒子群优化
Support vector machine （SVM）
Coronary heart disease （CHD）
Prediction
Particle swarm optimization （PSO）
作者简介
通讯作者。Email：yfang@scut．edu．cn
