ArticleScientific reports2020
An artificial neural network approach for predicting hypertension using NHANES data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 97 citations in OpenAlex.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Present and future of machine learning in breast surgery: systematic review.The British journal of surgery · 2022Pooled it
- Incorporating geospatial environmental exposure indicators in individual hypertension risk prediction: a multi-stage machine learning pipeline.Journal of exposure science & environmental epidemiology · 2026Article
- Machine learning models for chronic disease risks using complex survey data: the impact of sample weights.Frontiers in digital health · 2026Article
- Best Practices for Data Modernization Across the United States Public Health System: Scoping Review.Journal of medical Internet research · 2025Article
- Detecting, Characterizing, and Mitigating Implicit and Explicit Racial Biases in Health Care Datasets With Subgroup Learnability: Algorithm Development and Validation Study.Journal of medical Internet research · 2025Article
- Screening hypertension using non-laboratory risk factors with machine learning: a retrospective cross-sectional study in Indonesia.BMJ open · 2025Article
- Predicting major amputation risk in diabetic foot ulcers using comparative machine learning models for enhanced clinical decision-making.Scientific reports · 2025Article
- Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management.Hypertension (Dallas, Tex. : 1979) · 2025Review
- Two-Year Hypertension Incidence Risk Prediction in Populations in the Desert Regions of Northwest China: Prospective Cohort Study.Journal of medical Internet research · 2025Article
- Identify the underlying true model from other models for clinical practice using model performance measures.BMC medical research methodology · 2025Article
- Prediction of primary Hypertension in Primary Health Care Settings in Coastal Karnataka Using Artificial Neural Network.Current hypertension reviews · 2025Article
- Machine learning evaluation of a hypertension screening program in a university workforce over five years.Scientific reports · 2024Article
- Artificial Intelligence in Medical Metaverse: Applications, Challenges, and Future Prospects.Current medical science · 2024Review
- Artificial neural network inference analysis identified novel genes and gene interactions associated with skeletal muscle aging.Journal of cachexia, sarcopenia and muscle · 2024Article
- A smartphone application toward detection of systolic hypertension in underserved populations.Scientific reports · 2024Article
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- Application of artificial intelligence in hypertension.Clinical hypertension · 2024Review
- Article
- Recent developments in machine learning modeling methods for hypertension treatment.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper focus on a neural network classification model to estimate the association among gender, race, BMI, age, smoking, kidney disease and diabetes in hypertensive patients. It also shows that artificial neural network techniques applied to large clinical data sets may provide a meaningful data-driven approach to categorize patients for population health management, and support in the control and detection of hypertensive patients, which is part of the critical factors for diseases of the heart. Data was obtained from the National Health and Nutrition Examination Survey from 2007 to 2016. This paper utilized an imbalanced data set of 24,434 with (69.71%) non-hypertensive patients, and (30.29%) hypertensive patients. The results indicate a sensitivity of 40%, a specificity of 87%, precision of 57.8% and a measured AUC of 0.77 (95% CI [75.01-79.01]). This paper showed results that are to some degree more effectively than a previous study performed by the authors using a statistical model with similar input features that presents a calculated AUC of 0.73. This classification model can be used as an inference agent to assist the professionals in diseases of the heart field, and can be implemented in applications to assist population health management programs in identifying patients with high risk of developing hypertension.
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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.