ABSTRACT This review evaluates classical machine learning‐based hypertension prediction models, emphasizing their role in addressing global health burdens , particularly in low‐ and middle‐income countries. Hypertension affects over 1.28 billion people globally and contributes to cardiovascular disease and mortality. The review compares machine‐learning techniques with traditional methods, focusing on key datasets, evaluation metrics, and model development to advance early detection and effective hypertension management. The review used the PRISMA framework, using databases such as Google Scholar, PubMed, and IEEE explorer to identify studies published between 2020 and 2024 on machine learning techniques, predictive models, and early detection of hypertension based on relevance, methodological rigor, and inclusion criteria. The study analyzed hypertension prediction models across various countries, including the US, England, Korea, Japan, China, Indonesia, Thailand, India, Bangladesh, Nepal, and several African countries. The models' performance varied with AUC statistic values ranging from 0.6 to 0.9, indicating a wide range of predictive accuracy. Machine learning techniques generally reported higher performance metrics than traditional statistical methods. Risk factor heterogeneity was evident, with models like random forest, logistic regression, and gradient‐boosted trees showing high predictive accuracy. Emerging techniques like SMOTE (Synthetic Minority Oversampling Technique) and ensemble methods improved unbalanced data set performance. The review explores the potential of machine learning‐based hypertension prediction models in healthcare, highlighting their ability to accurately predict hypertension risk, tailor interventions to specific populations, and optimize healthcare resources in low‐ and middle‐income countries. However, challenges include data quality, model explainability, and ethical considerations. Despite these, ML integration offers scalable and cost‐effective solutions, especially in resource‐limited settings. Future research should focus on diverse datasets, advanced feature integration, and longitudinal validations.
Engda et al. (Fri,) studied this question.