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Published on: February 7, 2014
A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave
Zhi-Yu Luo1, Ji Cui1, Xiao-Juan Hu2
1Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai 201203, China.
Insights
Machine learning accurately classifies hypertension using pulse wave analysis, improving diagnostic accuracy and offering a new objective reference for traditional Chinese medicine. This method enhances hypertension risk assessment and clinical applications.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Medicine
- Cardiovascular Diagnostics
Background:
- Hypertension diagnosis traditionally relies on subjective measures.
- Pulse diagnosis in Traditional Chinese Medicine (TCM) offers dynamic insights but lacks objective quantification.
- Developing objective, data-driven methods for hypertension assessment is crucial.
Purpose of the Study:
- To utilize machine learning for classifying hypertensive and healthy individuals based on pulse wave data.
- To assess hypertension risk by analyzing dynamic changes in pulse wave signals.
- To provide an objective reference for the clinical application of TCM pulse diagnosis.
Main Methods:
- Collected data from 450 hypertensive and 479 healthy cases, including self-reported information and pulse wave data from a custom instrument (PDA-1).
- Developed machine learning models using questionnaire and pulse wave data as inputs.
- Applied K-means clustering to remove noise and analyze the impact on model accuracy, stability, and feature importance.
Main Results:
- Machine learning models demonstrated improved accuracy and Area Under the Curve (AUC) after noise reduction.
- Random Forest achieved 85.33% accuracy and 0.85 AUC; AdaBoost and Gradient Boosting reached 86.41% accuracy and 0.86 AUC.
- Support Vector Machine (SVM) accuracy increased from 79.57% to 83.15%, with AUC improving from 0.79 to 0.83.
Conclusions:
- Pulse wave-based analysis provides a valuable, objective method for hypertension diagnosis.
- Digital pulse wave diagnosis is feasible for dynamic hypertension evaluation.
- This research lays a foundation for TCM in the dynamic assessment of modern diseases and treatment efficacy.
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.
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