Zhu Yue, Wu Jianghua, Fang Ying
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2013 Dec;30(6):1180-5.
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 sigma 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.
基于体检获得的血压、血脂、血糖和尿酸数据，应用支持向量机（SVM）对中国南方人群中的冠心病（CHD）患者和非冠心病个体进行识别，以指导该疾病的进一步防治。首先，分别使用径向基核函数、线性核函数和多项式核函数构建支持向量机分类器。其次，通过粒子群优化算法（PSO）对支持向量机惩罚因子C和核参数sigma进行优化，然后用于冠心病的诊断和预测。与具有反向传播（BP）模型的人工神经网络、线性判别分析、逻辑回归方法和未优化的支持向量机的计算结果进行比较，我们的计算总体结果表明，优化后的径向基函数支持向量机（RBF-SVM）模型的分类性能优于其他分类器算法，准确率、灵敏度和特异性更高，分别为94.51%、92.31%和96.67%。因此，可以得出结论，支持向量机可作为辅助诊断冠心病的有效方法。
