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A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave
Joint Authors
Xu, Jia-Tuo
Tu, Li Ping
Qi, Zhen
Hu, Xiao-juan
Zhang, Zhi-feng
Luo, Zhi-yu
Cui, Ji
Liu, Hai-dan
Jiao, Wen
Zeng, Ling-zhi
Jing, Cong-cong
Qiao, Li-jie
Ma, Xu-xiang
Wang, Yu
Wang, Jue
Pai, Ching-Hsuan
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-11
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Luo, Zhi-yu& Cui, Ji& Hu, Xiao-juan& Tu, Li Ping& Liu, Hai-dan& Jiao, Wen…[et al.]. 2018. A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave. BioMed Research International،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1125551
Modern Language Association (MLA)
Luo, Zhi-yu…[et al.]. A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave. BioMed Research International No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1125551
American Medical Association (AMA)
Luo, Zhi-yu& Cui, Ji& Hu, Xiao-juan& Tu, Li Ping& Liu, Hai-dan& Jiao, Wen…[et al.]. A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1125551
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1125551