ArticleFrontiers in public health2021
Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method.
Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed, 62 citations in OpenAlex.
- Multi-horizon machine learning for population-level hypertension risk stratification in the UK Biobank.European heart journal. Digital health · 2026Article
- Non-laboratory-based cardiovascular risk assessment: 10-year absolute risk, predicted heart age and intervention requirements in a Nigerian population.BMC cardiovascular disorders · 2026Article
- Hypertension Detection Using Explainable Stacked Ensemble Machine Learning From Clinical and Physiological Data: A Comprehensive Study.Health science reports · 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
- A machine learning-based framework for predicting hypertension using serum hematological factors.Scientific reports · 2026Article
- Associations of the Mediterranean diet during pregnancy with impaired glucose tolerance and gestational diabetes: A national prospective cohort study in Lebanon.European journal of clinical nutrition · 2026Article
- Comparison of TyG indices and atherogenic index of plasma with hypertension in the PERSIAN Guilan cohort.Scientific reports · 2026Article
- Explainable machine learning for hypertension prevalence classification: a cross-sectional study in Hainan Province, China.Frontiers in cardiovascular medicine · 2026Article
- Machine-Learning-Based Prediction of Hypertension and Its Risk Factors Among Adults in the Northern Region of Bangladesh.Journal of diabetes research · 2026Article
- Sociodemographic and lifestyle determinants of blood pressure in adult Nigerians.International health · 2025Article
- Prediction of Personalised Hypertension Using Machine Learning in Indonesian Population.Journal of medical systems · 2025Article
- Screening hypertension using non-laboratory risk factors with machine learning: a retrospective cross-sectional study in Indonesia.BMJ open · 2025Article
- Temporal analysis of non-communicable diseases and NCD-HIV/AIDS comorbidity in Malawi: A 4-year retrospective study 2020-2022.Tropical medicine & international health : TM & IH · 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
- Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women.BMC women's health · 2025Article
- Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.Hypertension (Dallas, Tex. : 1979) · 2025Review
- Remote photoplethysmography for health assessment: a review informed by IntelliProve technology.Frontiers in digital health · 2025Review
- Environmental chemical exposures and a machine learning-based model for predicting hypertension in NHANES 2003-2016.BMC cardiovascular disorders · 2024Article
- Bilateral Matching Method for Business Resources Based on Synergy Effects and Incomplete Data.Entropy (Basel, Switzerland) · 2024Article
- Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension.Clinics and practice · 2024Review
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypertension is a widespread chronic disease. Risk prediction of hypertension is an intervention that contributes to the early prevention and management of hypertension. The implementation of such intervention requires an effective and easy-to-implement hypertension risk prediction model. This study evaluated and compared the performance of four machine learning algorithms on predicting the risk of hypertension based on easy-to-collect risk factors. A dataset of 29,700 samples collected through a physical examination was used for model training and testing. Firstly, we identified easy-to-collect risk factors of hypertension, through univariate logistic regression analysis. Then, based on the selected features, 10-fold cross-validation was utilized to optimize four models, random forest (RF), CatBoost, MLP neural network and logistic regression (LR), to find the best hyper-parameters on the training set. Finally, the performance of models was evaluated by AUC, accuracy, sensitivity and specificity on the test set. The experimental results showed that the RF model outperformed the other three models, and achieved an AUC of 0.92, an accuracy of 0.82, a sensitivity of 0.83 and a specificity of 0.81. In addition, Body Mass Index (BMI), age, family history and waist circumference (WC) are the four primary risk factors of hypertension. These findings reveal that it is feasible to use machine learning algorithms, especially RF, to predict hypertension risk without clinical or genetic data. The technique can provide a non-invasive and economical way for the prevention and management of hypertension in a large population.
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