Research graph
References from Machine learning-based models to predict the conversion of normal blood pressure to hypertension within 5-year follow-up. Local targets link to admitted publications; unresolved targets remain external evidence.
Predicting hypertension using machine learning: Findings from Qatar Biobank Study.
10.1371/journal.pone.0240370 · 2020 · External reference
Prevalence and associated risk factors of hypertension and pre-hypertension among the adult population: findings from the Dubai Household Survey, 2019
10.1186/s12872-022-02457-4 · 2022 · External reference
The association between bilirubin and hypertension among a Chinese ageing cohort: a prospective follow-up study
10.1186/s12967-022-03309-7 · 2022 · External reference
Prevalence of HTN in Iran: meta-analysis of published studies in 2004–2018
10.2174/1573402115666190118142818 · 2019 · External reference
Hypertension: A global health crisis
10.29328/journal.ach.1001027 · 2021 · External reference
Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project.
10.1371/journal.pone.0195344 · 2018 · External reference
Hypertension Silent killer.
1999 · External reference
Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries.
10.3389/fcvm.2022.839379 · 2022 · External reference
The global epidemiology of hypertension
10.1038/s41581-019-0244-2 · 2020 · External reference
The economic burden of cardiovascular disease and hypertension in low-and middle-income countries: a systematic review.
10.1186/s12889-018-5806-x · 2018 · External reference
Conducting research on the economics of hypertension to improve cardiovascular health
10.1016/j.amepre.2017.08.005 · 2017 · External reference
General cardiovascular risk profile for use in primary care: the Framingham Heart Study
10.1161/circulationaha.107.699579 · 2008 · External reference
2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines
2014 · External reference
Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease).
10.1016/j.jbi.2019.103257 · 2019 · External reference
What is machine learning? A primer for the epidemiologist
2019 · External reference
Machine learning for survival analysis: A survey
10.1145/3214306 · 2019 · External reference
Essential hypertension: an approach to its etiology and neurogenic pathophysiology
10.1155/2013/547809 · 2013 · External reference
Cohort Profile: The Fasa Adults Cohort Study (FACS): a prospective study of non-communicable diseases risks.
2023 · External reference
Scikit-learn: Machine learning in Python.
2011 · External reference
Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data
10.1016/j.ecolmodel.2019.06.002 · 2019 · External reference
LVQ-SMOTE—Learning Vector Quantization based Synthetic Minority Over-sampling Technique for biomedical data.
10.1186/1756-0381-6-16 · 2013 · External reference
editors. A review of feature selection methods with applications.
10.1109/mipro.2015.7160458 · 2015 · External reference
A unified approach to interpreting model predictions
2017 · External reference
An evaluation of feature selection methods for environmental data.
10.1016/j.ecoinf.2021.101224 · 2021 · External reference
Body composition predicts hypertension using machine learning methods: a cohort study
10.1038/s41598-023-34127-6 · 2023 · External reference
Machine learning algorithm for characterizing risks of hypertension, at an early stage in Bangladesh.
10.1016/j.dsx.2021.03.035 · 2021 · External reference
Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia.
10.1371/journal.pone.0289613 · 2023 · External reference
Development of a convenient and effective hypertension risk prediction model and exploration of the relationship between Serum Ferritin and Hypertension Risk: a study based on NHANES 2017-March 2020.
10.3389/fcvm.2023.1224795 · 2023 · External reference
Macroscopic hematuria as a risk factor for hypertension in ageing people with hemophilia and a family history of hypertension
10.1097/md.0000000000019339 · 2020 · External reference
Hypertension, haematuria and renal functioning in haemophilia—a cross-sectional study in Europe.
10.1111/hae.12847 · 2016 · External reference
Haematuria is not a risk factor of hypertension or renal impairment in patients with haemophilia
10.1111/hae.12921 · 2016 · External reference
Hematuria as a risk factor for progression of chronic kidney disease and death: findings from the Chronic Renal Insufficiency Cohort (CRIC) Study.
10.1186/s12882-018-0951-0 · 2018 · External reference
The role of renin-angiotensin-aldosterone system in the progression of chronic kidney disease
10.1111/j.1523-1755.2005.09911.x · 2005 · External reference
Hypertension: renin-angiotensin-aldosterone system alterations.
10.1161/circresaha.116.303587 · 2015 · External reference
The role of inflammation in hypertension: novel concepts
10.1016/j.cophys.2020.09.016 · 2021 · External reference
Anthropometric indices and the incidence of hypertension: a comparative analysis
10.1038/oby.2005.184 · 2005 · External reference
A comparison of body mass index and waist-to-hip ratio as indicators of hypertension risk in an urban Argentine population: a hospital-based study
10.1016/j.numecd.2005.03.001 · 2005 · External reference
A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0·5 could be a suitable global boundary value.
10.1017/s0954422410000144 · 2010 · External reference
Waist-to-height ratio index for predicting incidences of hypertension: the ARIRANG study
10.1186/s12889-018-5662-8 · 2018 · External reference
Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis.
10.1111/j.1467-789x.2011.00952.x · 2012 · External reference
Blood pressure change and risk of hypertension associated with parental hypertension: the Johns Hopkins Precursors Study
10.1001/archinte.168.6.643 · 2008 · External reference
Family history of hypertension enhances age-dependent rise in blood pressure, a 15-year follow-up, the Tampere adult population cardiovascular risk study.
10.1097/md.0000000000035366 · 2023 · External reference
Sex steroids receptors, hypertension, and vascular ageing.
10.1038/s41371-021-00576-7 · 2022 · External reference
The importance of gender to understand sex differences in cardiovascular disease
10.1016/j.cjca.2021.02.005 · 2021 · External reference
Sex Differences in the Prevalence, Outcomes and Management of Hypertension.
10.1007/s11906-022-01183-8 · 2022 · External reference
Gender differences in the association between socioeconomic status and hypertension in France: A cross-sectional analysis of the CONSTANCES cohort
10.1371/journal.pone.0231878 · 2020 · External reference
A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population
10.1038/s41598-022-27264-x · 2023 · External reference
