ArticleJournal of family & community medicine
Trend and predictive modeling of hypertension risk using multiyear community health surveillance data: Evidence from 2019 to 2021.
Article in Journal of family & community medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundHypertension remains a major global health burden, often undetected until complications occur. In Indonesia, evidence integrating multiyear surveillance data with machine learning (ML) to predict population-level risk remains limited. The objective of the study was to examine temporal trends in hypertension risk and develop predictive models at the population level. MATERIALS AND
methodsThis observational study utilized community health surveillance data on 196,951 adults (2019-2021). Blood pressure was classified using World Health Organization/Joint National Committee 7 criteria, with prehypertension and hypertension combined as abnormal (
resultsAbnormal blood pressure prevalence increased from 74.4% (2019) to 79.7% (2020) and 80.2% (2021). XGBoost demonstrated superior performance, achieving 78.3% accuracy and an area under the curve of 0.736, outperforming other models. SHAP analysis identified age, waist circumference, body mass index, and male sex as the strongest predictors of the risk of hypertension.
conclusionHypertension risk in Indonesia rose from 2019 to 2021. Interpretable ML models, particularly XGBoost, show potential for population-level risk stratification and early detection in community and primary healthcare settings.
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