{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T19:36:08Z","timestamp":1785785768469,"version":"3.56.0"},"reference-count":47,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Factors contributing to the development of hypertension exhibit significant variations across countries and regions. Our objective was to predict individuals at risk of developing hypertension within a 5-year period in a rural Middle Eastern area.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>This longitudinal study utilized data from the Fasa Adults Cohort Study (FACS). The study initially included 10,118 participants aged 35\u201370 years in rural districts of Fasa, Iran, with a follow-up of 3,000 participants after 5 years using random sampling. A total of 160 variables were included in the machine learning (ML) models, and feature scaling and one-hot encoding were employed for data processing. Ten supervised ML algorithms were utilized, namely logistic regression (LR), support vector machine (SVM), random forest (RF), Gaussian naive Bayes (GNB), linear discriminant analysis (LDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), extreme gradient boosting (XGB), cat boost (CAT), and light gradient boosting machine (LGBM). Hyperparameter tuning was performed using various combinations of hyperparameters to identify the optimal model. Synthetic Minority Over-sampling Technology (SMOTE) was used to balance the training data, and feature selection was conducted using SHapley Additive exPlanations (SHAP).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>Out of 2,288 participants who met the criteria, 251 individuals (10.9%) were diagnosed with new hypertension. The LGBM model (determined to be the optimal model) with the top 30 features achieved an AUC of 0.67, an f1-score of 0.23, and an AUC-PR of 0.26. The top three predictors of hypertension were baseline systolic blood pressure (SBP), gender, and waist-to-hip ratio (WHR), with AUCs of 0.66, 0.58, and 0.63, respectively. Hematuria in urine tests and family history of hypertension ranked fourth and fifth.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>ML models have the potential to be valuable decision-making tools in evaluating the need for early lifestyle modification or medical intervention in individuals at risk of developing hypertension.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0300201","type":"journal-article","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T17:28:26Z","timestamp":1710437306000},"page":"e0300201","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":8,"title":["Machine learning-based models to predict the conversion of normal blood pressure to hypertension within 5-year follow-up"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8196-5300","authenticated-orcid":true,"given":"Aref","family":"Andishgar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2128-0924","authenticated-orcid":true,"given":"Sina","family":"Bazmi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reza","family":"Tabrizi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maziyar","family":"Rismani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0055-6988","authenticated-orcid":true,"given":"Omid","family":"Keshavarzian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Babak","family":"Pezeshki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fariba","family":"Ahmadizar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,3,14]]},"reference":[{"issue":"10","key":"pone.0300201.ref001","doi-asserted-by":"crossref","first-page":"e0240370","DOI":"10.1371\/journal.pone.0240370","article-title":"Predicting hypertension using machine learning: Findings from Qatar Biobank Study.","volume":"15","author":"LA AlKaabi","year":"2020","journal-title":"PLoS One"},{"issue":"1","key":"pone.0300201.ref002","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s12872-022-02457-4","article-title":"Prevalence and associated risk factors of hypertension and pre-hypertension among the adult population: findings from the Dubai Household Survey, 2019","volume":"22","author":"H Mamdouh","year":"2022","journal-title":"BMC Cardiovascular Disorders"},{"issue":"1","key":"pone.0300201.ref003","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1186\/s12967-022-03309-7","article-title":"The association between bilirubin and hypertension among a Chinese ageing cohort: a prospective follow-up study","volume":"20","author":"C Tang","year":"2022","journal-title":"Journal of Translational Medicine"},{"issue":"2","key":"pone.0300201.ref004","doi-asserted-by":"crossref","first-page":"113","DOI":"10.2174\/1573402115666190118142818","article-title":"Prevalence of HTN in Iran: meta-analysis of published studies in 2004\u20132018","volume":"15","author":"MJ Oori","year":"2019","journal-title":"Current hypertension reviews"},{"key":"pone.0300201.ref005","doi-asserted-by":"crossref","first-page":"8","DOI":"10.29328\/journal.ach.1001027","article-title":"Hypertension: A global health crisis","volume":"5","author":"PA Berek","year":"2021","journal-title":"Ann Clin Hypertens"},{"issue":"4","key":"pone.0300201.ref006","doi-asserted-by":"crossref","first-page":"e0195344","DOI":"10.1371\/journal.pone.0195344","article-title":"Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project.","volume":"13","author":"S Sakr","year":"2018","journal-title":"PLoS One."},{"issue":"3","key":"pone.0300201.ref007","first-page":"41","article-title":"Hypertension Silent killer.","volume":"96","author":"R. 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