ArticleInternational journal of hypertension2025
Predicting Factors Associated With Uncontrolled Hypertension Using Machine Learning Methods: A Cross-Sectional Study in Western Iran.
Article in International journal of hypertension, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Machine learning prediction of hypertension integrating polygenic risk scores in inner Eurasian populations.Frontiers in cardiovascular medicine · 2026Article
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4 authors.
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Abstract
Uncontrolled hypertension is a major public health issue globally. This study aimed to uncover the factors contributing to uncontrolled hypertension using machine learning techniques. In this study, 303 adults with hypertension were included in this cross-sectional study. Data were collected using the Standard Health Literacy Questionnaire. Uncontrolled hypertension was defined as systolic blood pressure (BP) ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg on both days. Data were analyzed using percentages and chi-square tests. Four machine learning algorithms were employed in this study. The efficacy of these algorithms was assessed using several performance metrics, including accuracy, positive predictive value, sensitivity,
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