Application of Artificial Intelligence in the Establishment of an Association Model between Metabolic Syndrome, TCM Constitution, and the Guidance of Medicated Diet Care

Chien, Pei Li; Liu, Chi-Feng; Huang, Hui-Ting; Jou, Hei-Jen; Shih, Jin-Chung; Young, Ton-Ho; Wang, Yifeng; Liao, Pei-Hung

doi:10.1155/2021/5530717

Cited by 11 publications

(11 citation statements)

References 29 publications

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“…Arrows connecting two nodes represent that those two random variables are causally or unconditionally independent. If two nodes are not featured with arrows, the random variables are conditionally independent ( 19 ). The importance of the variables would be obtained.…”

Section: Methodsmentioning

confidence: 99%

Relationship Between Lipid Profiles and Hypertension: A Cross-Sectional Study of 62,957 Chinese Adult Males

Chen

Cheng

2022

Front. Public Health

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BackgroundPatterns of dyslipidemia and incidence of hypertension have been rarely reported in Asian populations with inconsistent findings. To accumulate further evidence in Asian populations, the study aimed to investigate the relationship between lipid profiles and hypertension in Chinese adult males.MethodsWe conducted a cross-sectional study based on the data from the DATADRYAD database. The overall population was divided into hypertensive and non-hypertensive groups based on baseline blood pressure levels. For continuous variables, Mann-Whitney test was performed between two groups, while Kruskal-Wallis and Dunn tests were used among multiple groups. The chi-square test was carried out for dichotomous variables. Spearman's correlation coefficient was employed to assess the association between systolic blood pressure (SBP), diastolic blood pressure (DBP) and lipid profiles, whereas the relationship between lipid profiles and the incidence of hypertension was evaluated using multivariate logistic regression. The Bayesian network (BN) model was adopted to investigate the relationship between clinical characteristics and hypertension, and the importance of related predictor to the incidence of hypertension was obtained to make conditional probability analysis.ResultsFinally, totally 62,957 participants were included in this study. In the lipid profiles, total cholesterol (TC), low-density cholesterol (LDL-c), and non- high-density lipoprotein cholesterol (non-HDL-c) were higher in the hypertensive population (p <0.001). In the fully multivariate model, for every 1 mg/dl increase in TC, LDL-c and non-HDL, the risk of hypertension increased by 0.2% [1.002 (1.001–1.003)], 0.1% [1.001 (1.000–1.002)], and 0.1% [1.001 (1.000–1.002)]. Meanwhile, HDL-c became positively associated with the incidence of hypertension (p for trend < 0.001) after adjusting for the body mass index (BMI), and 1 mg/dl increment in HDL-c increased the risk of hypertension by 0.2% [1.002 (1.000–1.002)] after fully adjusting for multiple variables. Furthermore, the BN showed that the importance of age, BMI, fasting plasma glucose (FPG), and TC to the effect of hypertension is 43.3, 27.2, 11.8, and 5.1%, respectively.ConclusionElevated TC, LDL-c, and non-HDL-c were related to incidence of hypertension in Chinese adult males, whereas triglycerides (TG) was not significantly associated. The relationship between HDL-c and hypertension incidence shifted from no association to a positive correlation after adjusting for the BMI. Moreover, the BN model displayed that age, the BMI, FPG, and TC were strongly associated with hypertension incidence.

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Section: Methodsmentioning

confidence: 99%

Relationship Between Lipid Profiles and Hypertension: A Cross-Sectional Study of 62,957 Chinese Adult Males

Chen

Cheng

2022

Front. Public Health

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“…Firstly, in the SPSS Modeler (version 18.0) model section, the BN model is constructed based on the tree augmented native (TAN) algorithm, and the parameter learning method is selected as a Bayesian adjustment for small cell counts ( 16 ). The arrow connecting two nodes indicates that the two random variables are causally or unconditionally independent; if there is no arrow connecting two nodes, it indicates that the random variables are conditionally independent ( 17 ). Meanwhile, the importance of the variables is obtained.…”

Section: Methodsmentioning

confidence: 99%

Differences in Lipid Profiles and Atherogenic Indices Between Hypertensive and Normotensive Populations: A Cross-Sectional Study of 11 Chinese Cities

Cheng

Wang

Chen

2022

Front. Cardiovasc. Med.

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BackgroundSeveral previous studies have reported that dyslipidemia is associated with the risk of hypertension, but these studies are mainly conducted in European and US populations, with a very few studies in the Asian population. Moreover, the effects of atherosclerotic indices, including atherogenic coefficient (AC) and atherogenic risk of plasma (AIP), on hypertension in Asians have not been well described so far.MethodsFrom 2010 to 2016, altogether 211,833 Chinese adults were ultimately recruited at the health centers in 11 Chinese cities (including Shanghai, Beijing, Nanjing, Suzhou, Shenzhen, Changzhou, Chengdu, Guangzhou, Hefei, Wuhan, and Nantong). Differences in continuous variables between the two groups were analyzed by the Mann–Whitney test, while those in categorical variables were examined by the Chi-squared test. Logistic regression was applied to evaluate the association between lipid profiles and the risk of hypertension. The predictive values of AC and AIP for the incidence of hypertension were analyzed using the area under the receiver operating characteristic (ROC) curve. Meanwhile, Bayesian network (BN) models were performed to further analyze the associations between the different covariates and the incidence of hypertension.ResultsA total of 117,056 participants were included in the final analysis. There were significant differences in baseline characteristics between normotension and hypertension groups (p < 0.001). In multivariate logistic regression, the risk of hypertension increased by 0.2% (1.002 [1.001–1.003]), 0.2% (1.002 [1.001–1.003]), and 0.2% (1.002 [1.001–1.003]) per 1 mg/dl increase in total cholesterol (TC), low-density lipoprotein (LDL), and non-high-density lipoprotein cholesterol (non-HDL-c), respectively. However, after adjusting for body mass index (BMI), an increase in HDL level was associated with a higher risk of hypertension (p for a trend < 0.001), and the risk of hypertension increased by 0.6% per 1 mg/dl increase in HDL-c (1.006 [1.003–1.008]). In women, AC had the highest predictive value for the incidence of hypertension with an area under the curve (AUC) of 0.667 [95% confidence interval (CI): 0.659–0.674]. BN models suggested that TC and LDL were more closely related to the incidence of hypertension.ConclusionsOverall, lipid profiles were significantly abnormal in the hypertensive population than in the normotensive population. TC and LDL were strongly associated with the incidence of hypertension. TC, LDL, and non-HDL-c levels show a positive association, HDL-c shows a negative association, while TG is not significantly associated with the risk of hypertension. After adjusting for BMI, HDL-c turns out to be positively associated with the risk of hypertension. In addition, AC has a good predictive value for the incidence of hypertension in women.

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“…The use of the probability of known events to infer the category of unknown data is the most significant feature in Bayesian classification [ 7 ], and when new sample data are added, a new classification model (probability) can be obtained by adjusting some probabilities; therefore, when data are added continuously, the classification performance improves. However, as the Bayesian classifier is constructed using a probability model, the reasons for the classification (EQ1) in some cases are difficult to explain [ 7 , 8 ]. A Bayesian network can be used to express the probability relationship between a disease and its related symptoms; when a certain symptom is known, a Bayesian network can be used to calculate the probability of various possible diseases [ 9 ].…”

Section: Methodsmentioning

confidence: 99%

“…A neural network is also an adaptive system for estimating functions. Learning capability is a nonlinear statistical data-modeling tool and is usually optimized by a learning method based on mathematical statistics [ 8 ]. A neural network is a machine learning method that imitates the operation of the brain and is mainly composed of neurons and synapses, where neurons are on different layers.…”

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning in Developing Decision-Making Support Models for Decompressed Vertebroplasty

2022

Self Cite

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Background: The common treatment methods for vertebral compression fractures with osteoporosis are vertebroplasty and kyphoplasty, and the result of the operation may be related to the value of various measurement data during the operation. Material and Method: This study mainly uses machine learning algorithms, including Bayesian networks, neural networks, and discriminant analysis, to predict the effects of different decompression vertebroplasty methods on preoperative symptoms and changes in vital signs and oxygen saturation in intraoperative measurement data. Result: The neural network shows better analysis results, and the area under the curve is >0.7. In general, important determinants of surgery include numbness and immobility of the lower limbs before surgery. Conclusion: In the future, this association model can be used to assist in decision making regarding surgical methods. The results show that different surgical methods are related to abnormal vital signs and may affect the length of hospital stay.

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Application of Artificial Intelligence in the Establishment of an Association Model between Metabolic Syndrome, TCM Constitution, and the Guidance of Medicated Diet Care

Cited by 11 publications

References 29 publications

Relationship Between Lipid Profiles and Hypertension: A Cross-Sectional Study of 62,957 Chinese Adult Males

Relationship Between Lipid Profiles and Hypertension: A Cross-Sectional Study of 62,957 Chinese Adult Males

Differences in Lipid Profiles and Atherogenic Indices Between Hypertensive and Normotensive Populations: A Cross-Sectional Study of 11 Chinese Cities

Application of Machine Learning in Developing Decision-Making Support Models for Decompressed Vertebroplasty

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