{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T22:30:24Z","timestamp":1780093824497,"version":"3.54.0"},"reference-count":47,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Clinical Hypertension"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2010term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine\u2010learning techniques could accurately predict hypertension risk using nutritional information. A cross\u2010sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine\u2010learning 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\u2010learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD\u00a0=\u00a022.16), 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.<\/jats:p>","DOI":"10.1111\/jch.14745","type":"journal-article","created":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T15:58:37Z","timestamp":1700150317000},"page":"1135-1144","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension"],"prefix":"10.1111","volume":"25","author":[{"given":"Alexander A.","family":"Huang","sequence":"first","affiliation":[{"name":"Cornell University  New York USA"},{"name":"Northwestern University Feinberg School of Medicine  Chicago USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3663-004X","authenticated-orcid":false,"given":"Samuel Y.","family":"Huang","sequence":"additional","affiliation":[{"name":"Cornell University  New York USA"},{"name":"Virginia 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