Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai 201203, China.
Shanghai Collaborative Innovation Center of Health Service in Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China.
Biomed Res Int. 2018 Nov 11;2018:2964816. doi: 10.1155/2018/2964816. eCollection 2018.
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).
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.
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.
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.
本研究利用机器学习对高血压组和健康组的脉象进行分类和预测，并通过观察脉象的动态变化评估高血压的风险，为中医脉诊的临床应用提供客观参考。
通过自行开发的 H20 问卷收集了 450 例高血压病例和 479 例健康对照的基本信息，通过自行开发的脉象诊断仪（PDA-1）采集了脉象信息。将 H20 问卷和脉象信息作为输入变量，得到不同的高血压机器学习分类模型。该方法旨在分析脉象对机器学习模型准确性和稳定性的影响，以及通过 K-均值去除噪声后高血压模型的特征贡献。
与去除噪声前的分类结果相比，准确性和曲线下面积（AUC）均有所提高。AdaBoost、梯度提升（Gradient Boosting）和随机森林（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，传统统计学和机器学习的重要特征是一致的。去除噪声后，AdaBoost 和 Gradient Boosting 中变化较大的特征是 h1/t1、w1/t、t、w2、h2、t1 和 t5（前 10 位）。传统统计学和机器学习的共同变量是 h1/t1、h5、t、Ad、BMI 和 t2。
基于脉象的高血压诊断方法具有重要的参考价值。鉴于数字脉象诊断的可行性和对高血压的动态评估，为中医在现代疾病诊断和疗效的动态评估中提供了研究方向和基础。
