ArticleJournal of clinical hypertension (Greenwich, Conn.)2023
Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension.
Article in Journal of clinical hypertension (Greenwich, Conn.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Prevalence of metabolic syndrome in patients with inflammatory bowel disease: a meta-analysis on a global scale.Journal of health, population, and nutrition · 2025Pooled it
- Revolutionizing Sleep Medicine: The Impact of Machine Learning on Diagnosis, Treatment, and Personalized Care.Health science reports · 2026Article
- Residential inequalities in hypertension medication use: a multivariate non-linear decomposition analysis.Journal of health, population, and nutrition · 2025Article
- Development, validation, and visualization of a machine learning-based predictive model for depression risk in sleep disorder patients.BMC psychiatry · 2025Article
- Exploring the association between volatile organic compound exposure and chronic kidney disease: evidence from explainable machine learning methods.Renal failure · 2025Article
- Interpretable machine learning model for low bone density screening in older adults using demographic and anthropometric data: findings from 2005 to 2020 NHANES.BMC medical informatics and decision making · 2025Article
- Predicting survival factor following suicide attempt in Iran: an ensemble machine learning technique.BMC psychiatry · 2025Article
- Construction and validation of a risk prediction model for chronic obstructive pulmonary disease (COPD): a cross-sectional study based on the NHANES database from 2009 to 2018.BMC pulmonary medicine · 2025Article
- The application of suitable sports games for junior high school students based on deep learning and artificial intelligence.Scientific reports · 2025Article
- Negative association between body roundness index and constipation: insights from NHANES.Journal of health, population, and nutrition · 2025Article
- Association between red blood cell distribution width-to-albumin ratio and depression: a cross-sectional analysis among US adults, 2011-2018.BMC psychiatry · 2025Article
- Implementation of an Integrated, Clinical Decision Support Tool at the Point of Antihypertensive Medication Refill Request to Improve Hypertension Management: Controlled Pre-Post Study.JMIR medical informatics · 2025Article
- Association of dietary calcium intake with chronic bronchitis and emphysema.Journal of health, population, and nutrition · 2025Article
- Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters.Frontiers in cardiovascular medicine · 2025Article
- Evaluation of the responsiveness pattern to caffeine through a smart data-driven ECG non-linear multi-band analysis.Heliyon · 2024Article
- Use machine learning models to identify and assess risk factors for coronary artery disease.PloS one · 2024Article
- Machine learning analysis of emerging risk factors for early-onset hypertension in the Tlalpan 2020 cohort.Frontiers in cardiovascular medicine · 2024Article
- Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension.Journal of clinical hypertension (Greenwich, Conn.) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long-term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine-learning techniques could accurately predict hypertension risk using nutritional information. A cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine-learning model of choice in this study due to its increased performance relative to other common methods within medical studies. Model prediction metrics (e.g., AUROC, Balanced Accuracy) were used to measure overall model efficacy, covariate Gain statistics (percentage each covariate contributes to the overall prediction) and SHapely Additive exPlanations (SHAP, method to visualize each covariate) were used to provide explanations to machine-learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD = 22.16), 4792 (50%) males, 4858 (50%) female, 3407 (35%) White patients, 2567 (27%) Black patients, 2108 (22%) Hispanic patients, and 981 (10%) Asian patients. From evaluation of model gain statistics, age was found to be the single strongest predictor of hypertension, with a gain of 53.1%. Additionally, demographic factors such as poverty and Black race were also strong predictors of hypertension, with gain of 4.33% and 4.18%, respectively. Nutritional Covariates contributed 37% to the overall prediction: Sodium, Caffeine, Potassium, and Alcohol intake being significantly represented within the model. Machine Learning can be used to predict 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.