ArticleFrontiers in nutrition2024
Machine learning models identify micronutrient intake as predictors of undiagnosed hypertension among rural community-dwelling older adults in Thailand: a cross-sectional study.
Article in Frontiers in nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Implementation of Health Empowerment Theory-Based Personalized Health Promotion in Village Health Volunteer Risk Group for Non-Communicable Diseases: A Mixed-Methods Study.Healthcare (Basel, Switzerland) · 2026Article
- Associations between food group intake and serum levels of selenium and other essential and toxic trace elements in adults.European journal of nutrition · 2026Article
- Machine Learning-Driven Precision Nutrition: A Paradigm Evolution in Dietary Assessment and Intervention.Nutrients · 2025Review
- Research progress on risk prediction models of physical restraint in the elderly: a narrative review.Frontiers in aging · 2025Review
- Public health implications of lifestyle and sociocultural determinants of stroke risk and serum biochemical markers among older adults in northern Thailand.Frontiers in neurologyArticle
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Authors and funding
5 authors.
Funding
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Abstract
Objective: To develop a predictive model for undiagnosed hypertension (UHTN) in older adults based on five modifiable factors [eating behaviors, emotion, exercise, stopping smoking, and stopping drinking alcohol (3E2S) using machine learning (ML) algorithms. Methods: The supervised ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB)] with SHapley Additive exPlanations (SHAP) prioritization and conventional statistics (χ Results: The χ Conclusion: ML indicates that salt intake, soybean consumption, and food/vitamin supplements are primary factors for UHTN classification in older adults.
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