Determination of risk factors for hypertension through the classification tree method

Akdag, Beyza; Fenkci, Semin; Değirmencioğlu, Serkan; Rota, Simin; Sermez, Yurdaer; Ankaralı, Handan

doi:10.1007/bf02850210

Cited by 13 publications

(5 citation statements)

References 25 publications

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“…Among the 20 selected features in this study, BMI, SBP, TG level, Cr level, LDL-C level, and glucose level had a strong effect on hypertension prediction and were included among the top 10 in the ranking of the feature importance for all three models. Similar to the results of previous studies, features such as age ( 27 – 29 ), BMI ( 28 , 30 ), diabetes status ( 28 ), Cr level ( 26 ), blood pressure ( 29 ), WC ( 31 ), smoking status ( 28 ), LDL-C level ( 26 , 28 ), HDL-C level ( 26 ), drinking ( 28 ), glucose level ( 32 ), TC level ( 26 , 27 ), exercise ( 33 ), salt intake ( 34 ), and TG level ( 27 ) were identified as predictors of hypertension in the risk assessment model of hypertension.…”

Section: Discussionsupporting

confidence: 89%

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Chen

Feng

Geng

et al. 2022

Front. Public Health

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ObjectiveThe prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the risk of hypertension for residents in primary care in Shanghai, China.MethodsA dataset of 40,261 subjects over the age of 35 years was extracted from Electronic Healthcare Records of 47 community health centers from 2017 to 2019 in the Pudong district of Shanghai. Embedded methods were applied for feature selection. Machine learning algorithms, XGBoost, random forest, and logistic regression analyses were adopted in the process of model construction. The performance of models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, accuracy and F1-score.ResultsThe XGBoost model outperformed the other two models and achieved an AUC of 0.765 in the testing set. Twenty features were selected to construct the model, including age, diabetes status, urinary protein level, BMI, elderly health self-assessment, creatinine level, systolic blood pressure measured on the upper right arm, waist circumference, smoking status, low-density lipoprotein cholesterol level, high-density lipoprotein cholesterol level, frequency of drinking, glucose level, urea nitrogen level, total cholesterol level, diastolic blood pressure measured on the upper right arm, exercise frequency, time spent engaged in exercise, high salt consumption, and triglyceride level.ConclusionsXGBoost outperformed random forest and logistic regression in predicting the risk of hypertension in primary care. The integration of this risk assessment model into primary care facilities may improve the prevention and management of hypertension in residents.

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Section: Discussionsupporting

confidence: 89%

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Chen

Feng

Geng

et al. 2022

Front. Public Health

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“…For instance, in a study by Chang et al, a different mining tool was employed, revealing significant results that underscored the association of triglycerides, creatinine, age, and uric acid with hypertension risk [ 20 ]. Similarly, Akdag et al utilized decision trees to identify BMI, waist/hip ratio, gender, and triglycerides as notable risk factors for hypertension [ 21 ]. Another study conducted in Qatar by AlKaabi et al yielded comparable results using random forest and logistic regression analyses, emphasizing the importance of age, physical activity, fruit and vegetable consumption, and diabetes history as critical predictors of hypertension [ 5 ].…”

Section: Discussionmentioning

confidence: 99%

The Evaluation of Ordinal Regression Model's Performance Through the Implementation of Multilayer Feed-Forward Neural Network: A Case Study of Hypertension

Adnan,

Ahmad,

Shahzad

et al. 2024

Cureus

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Background Hypertension, or high blood pressure, is a common medical condition that affects a significant portion of the global population. It is a major risk factor for cardiovascular diseases (CVD), stroke, and kidney disorders. Objective The objective of this study is to create and validate a model that combines bootstrapping, ordered logistic regression, and multilayer feed-forward neural networks (MLFFNN) to identify and analyze the factors associated with hypertension patients who also have dyslipidemia. Material and methods A total of 33 participants were enrolled from the Hospital Universiti Sains Malaysia (USM) for this study. In this study, advanced computational statistical modeling techniques were utilized to examine the relationship between hypertension status and several potential predictors. The RStudio (Posit, Boston, MA) software and syntax were implemented to establish the relationship between hypertension status and the predictors. Results The statistical analysis showed that the developed methodology demonstrates good model fitting through the value of predicted mean square error (MSE), mean absolute deviance (MAD), and accuracy. To evaluate model fitting, the data in this study was divided into distinct training and testing datasets. The findings revealed that the results strongly support the superior predictive capability of the hybrid model technique. In this case, five variables are considered: marital status, smoking status, systolic blood pressure, fasting blood sugar levels, and high-density lipoprotein levels. It is important to note that all of them affect the hazard ratio: marital status (β1, -17.12343343; p < 0.25), smoking status (β2, 1.86069121; p < 0.25), systolic blood pressure (β3, 0.05037332; p < 0.25), fasting blood sugar (β4, -0.53880322; p < 0.25), and high-density lipoprotein (β5, 5.38065556; p < 0.25). Conclusion This research aims to develop and extensively evaluate the hybrid approach. The statistical methods employed in this study using R language show that regression modeling surpasses R-squared values in predicting the mean square error. The study's conclusion provides strong evidence for the superiority of the hybrid model technique.

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“…They found that old age, body mass index and low education attainment are significant risk factors. Akdag et al (2006) applied the classification tree method to determine risk factors for hypertension among 1761 adults at the outpatient clinic in western Turkey between January 2002 and July 2004. They studied the effects of fourteen risk factors on hypertension, and their results revealed that body mass index, waist-to-hip ratio, sex, serum triglycerides, serum total cholesterol, hypertension in first-degree relatives, and saturated fat consumption are main risk factors.…”

Section: Related Workmentioning

confidence: 99%

Predicting hypertension without measurement: A non-invasive, questionnaire-based approach

Wang

Chen

et al. 2015

Expert Systems with Applications

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Determination of risk factors for hypertension through the classification tree method

Cited by 13 publications

References 25 publications

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

The Evaluation of Ordinal Regression Model's Performance Through the Implementation of Multilayer Feed-Forward Neural Network: A Case Study of Hypertension

Predicting hypertension without measurement: A non-invasive, questionnaire-based approach

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