2019
DOI: 10.1111/jch.13662
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Application of a decision tree to establish factors associated with a nomogram of aortic stiffness

Abstract: Aortic stiffness is a marker of vascular aging and may reflect occurrence of cardiovascular (CV) diseases. Aortic pulse wave velocity (PWV), a marker of aortic stiffness, can be measured by applanation tonometry. A nomogram of aortic stiffness was evaluated by the calculation of PWV index. Theoretical PWV can be calculated according to age, gender, mean blood pressure, and heart rate, allowing to form an individual PWV index [(measured PWV – theoretical PWV)/theoretical PWV]. The purpose of the present cross‐s… Show more

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Cited by 16 publications
(18 citation statements)
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“…In overall models, all cofounding factors remained significantly associated with AS, such as age, BMI, HR, mean BP, gender, fasting glucose, GFR, and tobacco status. These results were consistent with the literature ( 61 ). One main interesting point was the added value of TG/HDL compared to these factors.…”
Section: Discussionsupporting
confidence: 94%
“…In overall models, all cofounding factors remained significantly associated with AS, such as age, BMI, HR, mean BP, gender, fasting glucose, GFR, and tobacco status. These results were consistent with the literature ( 61 ). One main interesting point was the added value of TG/HDL compared to these factors.…”
Section: Discussionsupporting
confidence: 94%
“…Another applied data mining technology was the decision tree, which was easy to implement and interpret. The major purpose of this method is to make a predictive model for the target variable [20]. The main advantage of decision tree analysis is that it can transform the complex interaction of various variables into an organized ow chart, which can clearly identify related in uencing factors.…”
Section: Association Rule and Decision Treementioning
confidence: 99%
“…The classifier used a decision tree algorithm [14]. Based on the principle of minimizing the Gini index, a decision tree was generated using the CART (classification and regression tree) algorithm.…”
Section: Classification Using Decision Tree Algorithmmentioning
confidence: 99%