A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave
A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave
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DOI：
10.1155/2018/2964816
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发表时间：
2018-01-01
影响因子：
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通讯作者：
Xu, Jia-tuo
中科院分区：
文献类型：
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作者：
Luo, Zhi-yu;Cui, Ji;Xu, Jia-tuo
Objective.本研究利用机器学习对高血压组和健康组的脉搏波进行分类和预测，通过观察脉搏波的动态变化来评估高血压的风险，为中医脉诊的临床应用提供客观参考。法采用自行研制的H20问卷对450例高血压患者和479例健康体检者进行基本信息采集，并采用自行研制的PDA-1型脉象诊断仪采集脉搏波信息。以H20问卷和脉搏波信息为输入变量，获得不同的高血压机器学习分类模型。该方法旨在分析脉搏波对机器学习模型准确性和稳定性的影响，以及K-means去噪后高血压模型的特征贡献。结果.与去噪前的分类结果相比，分类精度和曲线下面积（AUC）均有所提高。AdaBoost、Gradient Boosting和Random Forest（RF）的准确率分别为86.41%、86.41%和85.33%。AUC分别为0.86、0.86和0.85。SVM的最大准确率从79.57%提高到83.15%，AUC稳定性从0.79提高到0.83。此外，传统统计和机器学习的重要特征是一致的。去除噪声后，AdaBoost和Gradient Boosting中变化较大的特征为h1/t1、w1/t、t、w2、h2、t1和t5（top10）。机器学习和传统统计学的常见变量是h1/t1，h5，t，Ad，BMI和t2。结论基于脉搏波的高血压诊断方法具有重要的参考价值。鉴于数字化脉搏波诊断和动态评价高血压的可行性，为中医药在现代疾病诊断和疗效动态评价方面提供了研究方向和基础。
Objective. In this study, machine learning was utilized to classify and predict pulse wave of hypertensive group and healthy group and assess the risk of hypertension by observing the dynamic change of the pulse wave and provide an objective reference for clinical application of pulse diagnosis in traditional Chinese medicine (TCM). Method. The basic information from 450 hypertensive cases and 479 healthy cases was collected by self-developed H20 questionnaires and pulse wave information was acquired by self-developed pulse diagnostic instrument (PDA-1). H20 questionnaires and pulse wave information were used as input variables to obtain different machine learning classification models of hypertension. This method was aimed at analyzing the influence of pulse wave on the accuracy and stability of machine learning model, as well as the feature contribution of hypertension model after removing noise by K-means. Result. Compared with the classification results before removing noise, the accuracy and the area under the curve (AUC) had been improved. The accuracy rates of AdaBoost, Gradient Boosting, and Random Forest (RF) were 86.41%, 86.41%, and 85.33%, respectively. AUC were 0.86, 0.86, and 0.85, respectively. The maximum accuracy of SVM increased from 79.57% to 83.15%, and the AUC stability increased from 0.79 to 0.83. In addition, the features of importance on traditional statistics and machine learning were consistent. After removing noise, the features with large changes were h1/t1, w1/t, t, w2, h2, t1, and t5 in AdaBoost and Gradient Boosting (top10). The common variables for machine learning and traditional statistics were h1/t1, h5, t, Ad, BMI, and t2. Conclusion. Pulse wave-based diagnostic method of hypertension has significant value in reference. In view of the feasibility of digital-pulse-wave diagnosis and dynamically evaluating hypertension, it provides the research direction and foundation for Chinese medicine in the dynamic evaluation of modern disease diagnosis and curative effect.