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Wang W, Zeng W, He S, Shi Y, Chen X, Tu L, Yang B, Xu J, Yin X. A new model for predicting the occurrence of polycystic ovary syndrome: Based on data of tongue and pulse. Digit Health 2023; 9:20552076231160323. [PMID: 37346080 PMCID: PMC10281487 DOI: 10.1177/20552076231160323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 02/12/2023] [Indexed: 09/20/2023] Open
Abstract
Background and objective Polycystic ovary syndrome is one of the most common types of endocrine and metabolic diseases in women of reproductive age that needs to be screened early and assessed non-invasively. The objective of the current study was to develop prediction models for polycystic ovary syndrome based on data of tongue and pulse using machine learning techniques. Methods A dataset of 285 polycystic ovary syndrome patients and 201 healthy women were investigated to identify the significant tongue and pulse parameters for predicting polycystic ovary syndrome. In this study, feature selection was performed using least absolute shrinkage and selection operator regression. Several machine learning algorithms (multilayer perceptron classifier, eXtreme gradient boosting classifier, and support vector machine) were used to construct the classification models to predict the presence of polycystic ovary syndrome. Results TB-L, TB-a, TB-b, TC-L, TC-a, h3, and h4/h1 in tongue and pulse parameters were statistically associated with polycystic ovary syndrome presence. Among the several machine learning techniques, the support vector machine model was optimal for the comprehensive evaluation of this dataset and deduced the area under the receiver operating characteristic curve, DeLong test, calibration curve, and decision curve analysis. Conclusion The machine learning model with tongue and pulse factors can predict the existence of polycystic ovary syndrome precisely.
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Affiliation(s)
- Weiying Wang
- Department of Gynecology and
Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Chinese Medicine,
Shanghai, P.R. China
| | - Weiwei Zeng
- Department of Gynecology and
Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Chinese Medicine,
Shanghai, P.R. China
| | - Shunli He
- Department of Gynecology and
Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Chinese Medicine,
Shanghai, P.R. China
| | - Yulin Shi
- Basic Medical College, Shanghai
University of Traditional Chinese Medicine, Shanghai, P.R. China
| | - Xinmin Chen
- Department of Gynecology and
Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Chinese Medicine,
Shanghai, P.R. China
| | - Liping Tu
- Basic Medical College, Shanghai
University of Traditional Chinese Medicine, Shanghai, P.R. China
| | - Bingyi Yang
- Department of Gynecology and
Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Chinese Medicine,
Shanghai, P.R. China
| | - Jiatuo Xu
- Basic Medical College, Shanghai
University of Traditional Chinese Medicine, Shanghai, P.R. China
| | - Xiuqi Yin
- Department of Gynecology and
Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Chinese Medicine,
Shanghai, P.R. China
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The Research and Development Thinking on the Status of Artificial Intelligence in Traditional Chinese Medicine. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:7644524. [PMID: 35547656 PMCID: PMC9085309 DOI: 10.1155/2022/7644524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 03/04/2022] [Accepted: 04/08/2022] [Indexed: 12/02/2022]
Abstract
With the rapid development and application of artificial intelligence (AI) in medical field, the diagnostic ways of human health and the social medical structures have changed. Based on the concept of holism and the theory of syndrome differentiation and treatment, TCM realizes comprehensive informatization and intelligence with the help of AI technology in data mining, intelligent diagnosis and treatment, intelligent learning, and decision-making. Furthermore, the intelligent research of TCM technology will further promote the improvement in TCM diagnosis and treatment rules and the leaping development of TCM intelligent instruments. In this article, we performed a systematic review of scientific literature about TCM and AI. Moreover, the practical problems of TCM intellectualization, the current situation and demand of TCM, and the influence of AI in the TCM field are discussed by searching for literature using TCM scientific databases, reference lists, expert consultation, and targeted websites. Finally, we look forward to the application prospects of AI and propose a possible future direction of intelligent TCM in the current health-care system in China.
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Cross-Channel Dynamic Weighting RPCA: A De-Noising Algorithm for Multi-Channel Arterial Pulse Signal. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12062931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Pulse wave analysis (PWA) has been widely used in the medical field. A novel multi-channel sensor is employed in arterial pulse acquisition and brings richer physiological information to PWA. However, the noise of this sensor is distributed in the main frequency band of the pulse signal, which seriously interferes with subsequent analyses and is difficult to eliminate by existing methods. This study proposes a cross-channel dynamic weighting robust principal component analysis algorithm. A channel-scaled factor technique is used to manipulate the weighting factors in the nuclear norm. This factor can adaptively adjust the weights among the channels according to the signal pattern of each channel, optimizing the feature extraction in multi-channel signals. A series of performance evaluations were conducted, and four well-known de-noising algorithms were used for comparison. The results reveal that the proposed algorithm achieved one of the best de-noising performances in the time and frequency domains. The mean of h1 in the amplitude relative error (ARE) was 23.4% smaller than for the WRPCA algorithm. Moreover, our algorithm could accelerate convergence and reduce the computational time complexity by approximately 34.6%. These results demonstrate the performance and efficiency of the algorithm. Meanwhile, the idea can be extended to other multi-channel physiological signal de-noising and feature extraction fields.
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A Study of Logistic Regression for Fatigue Classification Based on Data of Tongue and Pulse. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:2454678. [PMID: 35287309 PMCID: PMC8917949 DOI: 10.1155/2022/2454678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 01/05/2022] [Indexed: 12/02/2022]
Abstract
Methods The Tongue and Face Diagnosis Analysis-1 instrument and Pulse Diagnosis Analysis-1 instrument were used to collect the tongue image and sphygmogram of the subhealth fatigue population (n = 252) and disease fatigue population (n = 1160), and we mainly analyzed the tongue and pulse characteristics and constructed the classification model by using the logistic regression method. Results The results showed that subhealth fatigue people and disease fatigue people had different characteristics of tongue and pulse, and the logistic regression model based on tongue and pulse data had a good classification effect. The accuracies of models of healthy controls and subhealth fatigue, subhealth fatigue and disease fatigue, and healthy controls and disease fatigue were 68.29%, 81.18%, and 84.73%, and the AUC was 0.698, 0.882, and 0.924, respectively. Conclusion This study provided a new noninvasive method for the fatigue diagnosis from the perspective of objective tongue and pulse data, and the modern tongue diagnosis and pulse diagnosis have good application prospects.
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Shi Y, Yao X, Xu J, Hu X, Tu L, Lan F, Cui J, Cui L, Huang J, Li J, Bi Z, Li J. A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning. Front Physiol 2022; 12:708742. [PMID: 35197858 PMCID: PMC8859319 DOI: 10.3389/fphys.2021.708742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 11/03/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Fatigue is a common and subjective symptom, which is associated with many diseases and suboptimal health status. A reliable and evidence-based approach is lacking to distinguish disease fatigue and non-disease fatigue. This study aimed to establish a method for early differential diagnosis of fatigue, which can be used to distinguish disease fatigue from non-disease fatigue, and to investigate the feasibility of characterizing fatigue states in a view of tongue and pulse data analysis. METHODS Tongue and Face Diagnosis Analysis-1 (TFDA-1) instrument and Pulse Diagnosis Analysis-1 (PDA-1) instrument were used to collect tongue and pulse data. Four machine learning models were used to perform classification experiments of disease fatigue vs. non-disease fatigue. RESULTS The results showed that all the four classifiers over "Tongue & Pulse" joint data showed better performances than those only over tongue data or only over pulse data. The model accuracy rates based on logistic regression, support vector machine, random forest, and neural network were (85.51 ± 1.87)%, (83.78 ± 4.39)%, (83.27 ± 3.48)% and (85.82 ± 3.01)%, and with Area Under Curve estimates of 0.9160 ± 0.0136, 0.9106 ± 0.0365, 0.8959 ± 0.0254 and 0.9239 ± 0.0174, respectively. CONCLUSION This study proposed and validated an innovative, non-invasive differential diagnosis approach. Results suggest that it is feasible to characterize disease fatigue and non-disease fatigue by using objective tongue data and pulse data.
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Affiliation(s)
- Yulin Shi
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, Pudong, China
| | - Xinghua Yao
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, Pudong, China
| | - Jiatuo Xu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, Pudong, China
| | - Xiaojuan Hu
- Shanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Pudong, China
| | - Liping Tu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, Pudong, China
| | - Fang Lan
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, Pudong, China
| | - Ji Cui
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, Pudong, China
| | - Longtao Cui
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, Pudong, China
| | - Jingbin Huang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, Pudong, China
| | - Jun Li
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, Pudong, China
| | - Zijuan Bi
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, Pudong, China
| | - Jiacai Li
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, Pudong, China
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Shi Y, Hu X, Cui J, Li J, Bi Z, Li J, Fu H, Wang Y, Cui L, Xu J. Correlation Analysis of Data of Tongue and Pulse in Patients With Disease Fatigue and Sub-health Fatigue. INQUIRY: THE JOURNAL OF HEALTH CARE ORGANIZATION, PROVISION, AND FINANCING 2022; 59:469580211060781. [PMID: 35112891 PMCID: PMC8819780 DOI: 10.1177/00469580211060781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Fatigue is one of the most common subjective symptoms of abnormal health state,
there is still no reliable and stable evaluation method to distinguish disease
fatigue and non-disease fatigue. Studies have shown that tongue diagnosis and
pulse diagnosis are the reflection of overall state of the body. This study aims
to explore the distribution rules and correlation of data of tongue and pulse in
population with disease fatigue and sub-health fatigue and provide a new method
of clinical diagnosis of fatigue from the perspective of tongue diagnosis and
pulse diagnosis. In this study, a total of 736 people were selected and divided
into healthy controls (n = 250), sub-health fatigue group (n = 242), and disease
fatigue group (n = 244). TFDA-1 tongue diagnosis instrument and PDA-1 pulse
diagnosis instrument were used to collect tongue image and sphygmogram, simple
correlation analysis and canonical correlation analysis were used to analyze the
correlation of tongue and pulse data about the two groups of fatigue people. The
study had shown that tongue and pulse data could provide a certain reference for
the diagnosis of different types of fatigue, tongue and pulse data in disease
fatigue and sub-health fatigue population had different distribution rules, and
there was a simple correlation and canonical correlation in the disease fatigue
population, the coefficient of canonical correlation was .649 (P <.05).
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Affiliation(s)
- Yulin Shi
- Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiaojuan Hu
- Shanghai Collaborative Innovation Center of Health Service in Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ji Cui
- Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jun Li
- Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zijuan Bi
- Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiacai Li
- Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hongyuan Fu
- Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yu Wang
- Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Longtao Cui
- Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiatuo Xu
- Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Shi Y, Hu X, Cui J, Cui L, Huang J, Ma X, Jiang T, Yao X, Lan F, Li J, Bi Z, Li J, Wang Y, Fu H, Wang J, Lin Y, Bai J, Guo X, Tu L, Xu J. Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population. BMC Med Inform Decis Mak 2021; 21:72. [PMID: 33627103 PMCID: PMC7905588 DOI: 10.1186/s12911-021-01410-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 01/28/2021] [Indexed: 12/19/2022] Open
Abstract
Background Fatigue is a kind of non-specific symptom, which occurs widely in sub-health and various diseases. It is closely related to people's physical and mental health. Due to the lack of objective diagnostic criteria, it is often neglected in clinical diagnosis, especially in the early stage of disease. Many clinical practices and researches have shown that tongue and pulse conditions reflect the body's overall state. Establishing an objective evaluation method for diagnosing disease fatigue and non-disease fatigue by combining clinical symptom, index, and tongue and pulse data is of great significance for clinical treatment timely and effectively. Methods In this study, 2632 physical examination population were divided into healthy controls, sub-health fatigue group, and disease fatigue group. Complex network technology was used to screen out core symptoms and Western medicine indexes of sub-health fatigue and disease fatigue population. Pajek software was used to construct core symptom/index network and core symptom-index combined network. Simultaneously, canonical correlation analysis was used to analyze the objective tongue and pulse data between the two groups of fatigue population and analyze the distribution of tongue and pulse data. Results Some similarities were found in the core symptoms of sub-health fatigue and disease fatigue population, but with different node importance. The node-importance difference indicated that the diagnostic contribution rate of the same symptom to the two groups was different. The canonical correlation coefficient of tongue and pulse data in the disease fatigue group was 0.42 (P < 0.05), on the contrast, correlation analysis of tongue and pulse in the sub-health fatigue group showed no statistical significance. Conclusions The complex network technology was suitable for correlation analysis of symptoms and indexes in fatigue population, and tongue and pulse data had a certain diagnostic contribution to the classification of fatigue population.
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Affiliation(s)
- Yulin Shi
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Xiaojuan Hu
- Shanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Ji Cui
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Longtao Cui
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Jingbin Huang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Xuxiang Ma
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Tao Jiang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Xinghua Yao
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Fang Lan
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Jun Li
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Zijuan Bi
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Jiacai Li
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Yu Wang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Hongyuan Fu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Jue Wang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Yanting Lin
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Jingxuan Bai
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Xiaojing Guo
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China
| | - Liping Tu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China.
| | - Jiatuo Xu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong, Shanghai, China.
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Fourier Series Analysis for Novel Spatiotemporal Pulse Waves: Normal, Taut, and Slippery Pulse Images. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2019; 2019:5734018. [PMID: 31885653 PMCID: PMC6900951 DOI: 10.1155/2019/5734018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 10/23/2019] [Indexed: 11/29/2022]
Abstract
In this article, a three-dimensional pulse image (3DPI) instead of a one-dimensional temporal pulse wave is studied to elucidate its spatiotemporal characteristics. To check the spatial and temporal properties of 3DPI, adopted is Fourier series, in which a ratio (r) is defined as one amplitude divided by the sum of the first three amplitudes of harmonics. A ratio sequence is constituted from 70 to 90 ratios in a heartbeat with 70–90 3DPIs by sampling. Twenty-four subjects (14 males and 10 females with age of 22.2 ± 3.7 years, 20.4 ± 1.4 BMI, and 112.1 ± 4.7 mmHg systolic blood pressure) are involved in this research. There are significant statistical differences in the groups of the normal, taut, and slippery 3DPIs by the first harmonic ratio average (r1¯) and ratio difference (Δr1) produced from the ratio sequence. The proposed method of this study gives us a novel viewpoint to clarify the spatiotemporal characteristics of pulse images, which can translate and quantize the pulse feeling in Chinese medicine texts.
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A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave. BIOMED RESEARCH INTERNATIONAL 2018; 2018:2964816. [PMID: 30534557 PMCID: PMC6252205 DOI: 10.1155/2018/2964816] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 10/05/2018] [Accepted: 10/28/2018] [Indexed: 12/28/2022]
Abstract
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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