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  • 杨晶东,孙磊明,燕海霞.最小二乘支持向量机算法在中医临床脉图参数-血压预测模型的应用[J].第二军医大学学报,2019,40(5):492-496    [点击复制]
  • YANG Jing-dong,SUN Lei-ming,YAN Hai-xia.Application of least square support vector machine algorithm in clinical pulse diagram parameter-blood pressure prediction model of traditional Chinese medicine[J].Acad J Sec Mil Med Univ,2019,40(5):492-496   [点击复制]
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最小二乘支持向量机算法在中医临床脉图参数-血压预测模型的应用
杨晶东1,孙磊明1*,燕海霞2
0
(1. 上海理工大学光电信息与计算机工程学院自主机器人实验室, 上海 200093;
2. 上海中医药大学基础医学院中医诊断教研室, 上海 201203
*通信作者)
摘要:
目的 提出基于最小二乘支持向量机(LSSVM)算法的学习模型,以提高中医临床血压数据预测的准确度和效率。方法 将LSSVM学习模型应用于中医临床血压数据预测。用LSSVM等式约束代替支持向量机不等式约束,将二次规划问题转化为线性方程求解问题,降低计算复杂性,加快算法收敛速度。收集320例患者的临床脉图参数及血压数据,以其中300例样本作为训练样本,训练得到LSSVM学习模型,以其余20例样本作为测试数据,用得到的LSSVM学习模型根据患者的脉图参数预测血压数据。结果 实验证明,LSSVM学习模型对血压数据有较好的预测准确度。其中基于多项式核函数的LSSVM学习模型较基于径向基核函数LSSVM学习模型表现出更好的学习和预测能力,基于多项式核函数的LSSVM学习模型中收缩压、舒张压、平均动脉压预测结果的平均预测误差分别为7.88%、8.40%、6.67%,低于基于径向基核函数的LSSVM学习模型的预测误差(分别为7.95%、9.70%、7.48%)。结论 本实验提出的基于LSSVM的学习模型仅通过患者的临床脉图参数就可预测患者血压数据,对中医学临床诊断有一定的参考价值。
关键词:  中国传统医学  脉图参数  血压  支持向量机  核函数  最小二乘法分析
DOI:10.16781/j.0258-879x.2019.05.0492
投稿时间:2018-10-25修订日期:2018-11-16
基金项目:国家自然科学基金(61374039),上海市自然科学基金(15ZR1429100),沪江基金(C14002).
Application of least square support vector machine algorithm in clinical pulse diagram parameter-blood pressure prediction model of traditional Chinese medicine
YANG Jing-dong1,SUN Lei-ming1*,YAN Hai-xia2
(1. Autonomous Robot Lab, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. Department of Traditional Chinese Medicine Diagnosis, Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
*Corresponding author)
Abstract:
Objective To propose a learning model based on least square support vector machine (LSSVM) algorithm to improve the accuracy and efficiency for predicting clinical blood pressure data of traditional Chinese medicine (TCM). Methods The LSSVM learning model was used to predict the clinical blood pressure of TCM. By replacing the inequality constraints of support vector machine with LSSVM equality constraints, the quadratic programming problem was transformed into a linear equation solution problem to reduce computational complexity and speed up algorithm convergence. The clinical pulse diagram parameters and blood pressure data of 320 patients were collected. Three hundred of them were used as training samples, the remaining 20 samples were used as test data. The LSSVM learning model was used to predict blood pressure data according to the pulse diagram parameters of the patients. Results Experimental results showed that the LSSVM learning model had high prediction accuracy for blood pressure data. The LSSVM learning model based on polynomial kernel function had better learning and prediction abilities than the LSSVM learning model based on radial basis kernel function. The mean prediction errors of systolic blood pressure, diastolic blood pressure and mean arterial pressure obtained by the LSSVM learning model based on polynomial kernel function were 7.88%, 8.40% and 6.67%, respectively, which were lower than those obtained by the LSSVM learning model based on radial basis kernel function (7.95%, 9.70% and 7.48%, respectively). Conclusion The LSSVM learning model proposed in this experiment can be used to predict the blood pressure data of patients only by the clinical pulse diagram parameters, and is a good reference for clinical diagnosis of TCM.
Key words:  traditional Chinese medicine  pulse diagram parameters  blood pressure  support vector machine  kernel function  least-squares analysis