ArticleJournal of obesity2014
Predicting increased blood pressure using machine learning.
Article in Journal of obesity, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 117 citations in OpenAlex.
- Essential dataset features in a successful obesity registry: a systematic review.International health · 2025Pooled it
- Assessment of Blood Pressure Using Only a Smartphone and Machine Learning Techniques: A Systematic Review.Frontiers in cardiovascular medicine · 2022Pooled it
- Predicting blood pressure from physiological index data using the SVR algorithm.BMC bioinformatics · 2019Trial
- Hypertension Detection Using Explainable Stacked Ensemble Machine Learning From Clinical and Physiological Data: A Comprehensive Study.Health science reports · 2026Article
- Simulating a Specialist's Treatment Experience for Hypertension Using Deep Neural Networks.Journal of clinical hypertension (Greenwich, Conn.) · 2025Article
- A machine learning approach to predict hypertension using cross-sectional & two years follow up data from a health & demographic cohort of Assam, North East India.The Indian journal of medical research · 2025Article
- Two-Year Hypertension Incidence Risk Prediction in Populations in the Desert Regions of Northwest China: Prospective Cohort Study.Journal of medical Internet research · 2025Article
- Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence.Frontiers in physiology · 2025Article
- Digitalization of hypertension management: a paradigm shift.Naunyn-Schmiedeberg's archives of pharmacology · 2024Review
- Combination of Machine Learning Techniques to Predict Overweight/Obesity in Adults.Journal of personalized medicine · 2024Article
- Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review.Cureus · 2024Review
- Article
- Risk factor analysis and risk prediction study of obesity in steelworkers: model development based on an occupational health examination cohort dataset.Lipids in health and disease · 2024Article
- Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction.Diagnostics (Basel, Switzerland) · 2023Article
- An Innovative Random-Forest-Based Model to Assess the Health Impacts of Regular Commuting Using Non-Invasive Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve.Journal of cardiovascular development and disease · 2023Review
- Combating hypertension beyond genome-wide association studies: Microbiome and artificial intelligence as opportunities for precision medicine.Cambridge prisms. Precision medicine · 2023Review
- Article
- Machine Learning in Hypertension Detection: A Study on World Hypertension Day Data.Journal of medical systems · 2022Article
- Empirical analyses and simulations showed that different machine and statistical learning methods had differing performance for predicting blood pressure.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R (2) (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R (2) (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.