The application of a decision tree to establish the parameters associated with hypertension

Tayefi, Maryam; Esmaeili, Habibollah; Karimian, Maryam Saberi; Zadeh, Alireza Amirabadi; Ebrahimi, Mahmoud; Safarian, Mohammad; Nematy, Mohsen; Parizadeh, Seyed Mohammad Reza; Ferns, Gordon A.; Ghayour‐Mobarhan, Majid

doi:10.1016/j.cmpb.2016.10.020

Cited by 79 publications

(48 citation statements)

References 20 publications

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“…Variables that have the best rate of splitting criterion are selected as staying in the model. In the decision tree, the first variable or root node is the most important factor and other variables can be classified in order to importance . It can be stated also that the root node is the variable that can divide the whole population with the highest information gain.…”

Section: Methodsmentioning

confidence: 99%

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Application of a decision tree to establish factors associated with a nomogram of aortic stiffness

Vallée¹,

Safar²,

Blacher³

2019

J of Clinical Hypertension

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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‐sectional study was to investigate the determinants of the PWV index, by applying a decision tree. A cross‐sectional study was conducted from 2012 to 2017, and 597 individuals were included. A training decision tree was constructed based on seventy percent of these subjects (N = 428). The remaining 30% (N = 169) were used as the testing dataset to evaluate the performance of the decision trees. The input variables for the models were clinical and biochemical parameters. The different input variables remained in the model were diabetes, tobacco status, carotid plaque, albuminuria, C‐reactive protein, total cholesterol, BMI, and previous CV diseases. For the validation decision model, the sensitivity, specificity, and accuracy values for identifying the related risk factors of PWV index were 70%, 78%, and 0.73. Since determinants of PWV index were all well‐accepted CV risk factors, a nomogram of aortic stiffness could be considered as an integrator of CV risk factors on their duration of exposure and could be utilized to develop future programs for CV risk assessment and reduction strategies.

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Section: Methodsmentioning

confidence: 99%

“…Data mining is a retrospective computational method for extracting knowledge from large databases. Data mining algorithms were applied to define new models for predicting the risk factors of hypertension . Decision tree is easy to implement and interpret.…”

Section: Introductionmentioning

confidence: 99%

Application of a decision tree to establish factors associated with a nomogram of aortic stiffness

Vallée¹,

Safar²,

Blacher³

2019

J of Clinical Hypertension

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“…A total of 15,323 records were initially examined and analyzed for potential construction of a decision tree approach. In order to meet the strict criteria for building data mining algorithms, the dataset in which certain variables were missing cannot be used since the decision tree approach will not work with missing data points [21]. In doing so, 28.6% of the total data (4348 out of 15,323 records) failed to meet the criteria for data selection and was thus excluded accordingly.…”

Section: Data Selectionmentioning

confidence: 99%

Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach

Pei

Zhang

et al. 2019

Journal of Diabetes Research

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Background Diabetes mellitus is a chronic disease with a steadfast increase in prevalence. Due to the chronic course of the disease combining with devastating complications, this disorder could easily carry a financial burden. The early diagnosis of diabetes remains as one of the major challenges medical providers are facing, and the satisfactory screening tools or methods are still required, especially a population- or community-based tool. Methods This is a retrospective cross-sectional study involving 15,323 subjects who underwent the annual check-up in the Department of Family Medicine of Shengjing Hospital of China Medical University from January 2017 to June 2017. With a strict data filtration, 10,436 records from the eligible participants were utilized to develop a prediction model using the J48 decision tree algorithm. Nine variables, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work-related stress, and salty food preference, were considered. Results The accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC) value for identifying potential diabetes were 94.2%, 94.0%, 94.2%, and 94.8%, respectively. The structure of the decision tree shows that age is the most significant feature. The decision tree demonstrated that among those participants with age ≤ 49, 5497 participants (97%) of the individuals were identified as nondiabetic, while age > 49, 771 participants (50%) of the individuals were identified as nondiabetic. In the subgroup where people were 34 < age ≤ 49 and BMI ≥ 25, when with positive family history of diabetes, 89 (92%) out of 97 individuals were identified as diabetic and, when without family history of diabetes, 576 (58%) of the individuals were identified as nondiabetic. Work-related stress was identified as being associated with diabetes. In individuals with 34 < age ≤ 49 and BMI ≥ 25 and without family history of diabetes, 22 (51%) of the individuals with high work-related stress were identified as nondiabetic while 349 (88%) of the individuals with low or moderate work-related stress were identified as not having diabetes. Conclusions We proposed a classifier based on a decision tree which used nine features of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of diabetes. The classifier indicates that a decision tree analysis can be successfully applied to screen diabetes, which will support clinical practitioners for rapid diabetes identification. The model provides a means to target the prevention of diabetes which could reduce the burden on the health system through effective case management.

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“…The sum is computed over m classes. In the DT, the first variable or root node is the most important variable and other variables would be in tree according to their importance . It can be stated also that the root node is the variable that can divide the whole population with the highest information gain.…”

Section: Methodsmentioning

confidence: 99%

“…In the DT, the first variable or root node is the most important variable and other variables would be in tree according to their importance. 15,25 It can be stated also that the root node is the variable that can divide the whole population with the highest information gain. It is a common approach in data mining methods to divide the data set into two parts; a training data set, generally 70% of the subjects, and the testing dataset, 30% of the subjects.…”

Section: Decision Treementioning

confidence: 99%

Determinants of the magnitude of response to vitamin D supplementation in adolescent girls identified using a decision tree algorithm

et al. 2019

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Vitamin D (VitD) supplementation is an inexpensive and effective approach for improving VitD insufficiency/deficiency. However, the response to supplementation, with respect to the increase in serum 25(OH)D level varies between individuals. In this study, we have assessed the factors associated with the response to VitD supplementation using a decision‐tree algorithm. Serum VitD levels, pre‐ and post‐VitD supplementation was used as the determinant of responsiveness. The model was validated by constructing a receiver operating characteristic curve. Serum VitD at baseline levels was at the apex of the tree in our model, followed by serum low‐density lipoprotein cholesterol and triglyceride, age, waist‐hip ratio, and high‐density lipoprotein cholesterol. Our model suggests that these determinants of responsiveness to VitD supplementation had sensitivity, specificity, and accuracy, 59.4, 75.8 and 69.3%, respectively. The decision tree model appears to be a relatively accurate, specific, and sensitive approach for identifying the factors associated with response to VitD supplementation.

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The application of a decision tree to establish the parameters associated with hypertension

Cited by 79 publications

References 20 publications

Application of a decision tree to establish factors associated with a nomogram of aortic stiffness

Application of a decision tree to establish factors associated with a nomogram of aortic stiffness

Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach

Determinants of the magnitude of response to vitamin D supplementation in adolescent girls identified using a decision tree algorithm

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