ArticleJournal of clinical hypertension (Greenwich, Conn.)2020
Highly precise risk prediction model for new-onset hypertension using artificial intelligence techniques.
Article in Journal of clinical hypertension (Greenwich, Conn.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 3 syntheses or guidelines pooled it, 119 citations in OpenAlex.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Predictive variables and diagnostic performance of cross-sectional models for hypertension detection: a systematic review.Frontiers in cardiovascular medicine · 2025Pooled it
- Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis.PloS one · 2024Pooled it
- Multi-horizon machine learning for population-level hypertension risk stratification in the UK Biobank.European heart journal. Digital health · 2026Article
- Development and validation of a hypertension risk prediction model using electronic health records data from general practices in Hampshire and Isle of Wight integrated care system.BMC cardiovascular disorders · 2026Article
- Identifying risk patterns for sudden cardiac death in athletes: A clustering and principal component analysis approach.PloS one · 2026Article
- Review
- Development and Validation of an Incident Hypertension Risk Prediction Model for Young Adults.Journal of the American Heart Association · 2025Article
- Combating cardiovascular disease disparities: The potential role of artificial intelligence.American journal of preventive cardiology · 2025Article
- Artificial intelligence in retinal image analysis for hypertensive retinopathy diagnosis: a comprehensive review and perspective.Visual computing for industry, biomedicine, and art · 2025Review
- Construction of a predictive model for type 2 diabetes mellitus with coexisting hypertension: A cross-sectional study.Medicine · 2025Observational
- Exploring the integration of medical and preventive chronic disease health management in the context of big data.Frontiers in public health · 2025Review
- Risk factors for comorbid hypertension in elderly patients with type 2 diabetes.American journal of translational research · 2025Article
- Machine learning evaluation of a hypertension screening program in a university workforce over five years.Scientific reports · 2024Article
- Predicting new cases of hypertension in Swedish primary care with a machine learning tool.Preventive medicine reports · 2024Article
- Precise risk-prediction model including arterial stiffness for new-onset atrial fibrillation using machine learning techniques.Journal of clinical hypertension (Greenwich, Conn.) · 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
- Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank.Journal of clinical medicine · 2024Article
- Application of artificial intelligence in hypertension.Clinical hypertension · 2024Review
- Development of risk models of incident hypertension using machine learning on the HUNT study data.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypertension is a significant public health issue. The ability to predict the risk of developing hypertension could contribute to disease prevention strategies. This study used machine learning techniques to develop and validate a new risk prediction model for new-onset hypertension. In Japan, Industrial Safety and Health Law requires employers to provide annual health checkups to their employees. We used 2005-2016 health checkup data from 18 258 individuals, at the time of hypertension diagnosis [Year (0)] and in the two previous annual visits [Year (-1) and Year (-2)]. Data were entered into models based on machine learning methods (XGBoost and ensemble) or traditional statistical methods (logistic regression). Data were randomly split into a derivation set (75%, n = 13 694) used for model construction and development, and a validation set (25%, n = 4564) used to test performance of the derived models. The best predictor in the XGBoost model was systolic blood pressure during cardio-ankle vascular index measurement at Year (-1). Area under the receiver operator characteristic curve values in the validation cohort were 0.877, 0.881, and 0.859 for the XGBoost, ensemble, and logistic regression models, respectively. We have developed a highly precise prediction model for future hypertension using machine learning methods in a general normotensive population. This could be used to identify at-risk individuals and facilitate earlier non-pharmacological intervention to prevent the future development of 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.