Machine learning approaches in identifying factors associated with hypertension and undiagnosed hypertension in adults in rural areas of Bangladesh

BACKGROUND: Hypertension is a major cause of death and disability, and undiagnosed cases are particularly dangerous as they can cause severe damage without timely treatment. The aim of the study was to identify risk factors for hypertension and undiagnosed hypertension in rural areas of Bangladesh using advanced Machine Learning (ML) algorithms. METHODS: This study involved 1,603 respondents, selected through a cross-sectional survey using a multistage cluster random sampling technique. Four ML algorithms, including Gradient Booster (GB), Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM), were used in this study. Risk factors for hypertension and undiagnosed hypertension were identified using the best-performing ML model, selected based on metrics such as accuracy, sensitivity, specificity, precision, F1 score, receiver operating characteristics-area under the curve (ROC-AUC), and calibration plot. RESULTS: The prevalence of hypertension was 15.5%, slightly higher than the 15.4% for undiagnosed hypertension. In predicting the risk of both hypertension and undiagnosed hypertension, the LR model outperformed other ML models across most evaluation metrics. For hypertension, it achieved higher performance in terms of precision (0.580), F1 score (0.550), ROC-AUC (0.729; 95% CI: 0.677-0.779), and calibration. Similarly, for undiagnosed hypertension, the LR model showed better precision (0.580), ROC-AUC (0.596; 95% CI: 0.537-0.654), and calibration compared to other models. The risk factors for hypertension and undiagnosed hypertension differed notably. Key risk factors for undiagnosed hypertension included being overweight or obese, the absence of chronic diseases or cardiovascular disease (CVD), being male, non-use of tobacco, older age (above 50 years), being currently married, non-smoking status, having diabetes, and having no formal education. CONCLUSION: The findings emphasize the urgent need for enhanced national and regional public health initiatives to improve the detection and awareness of hypertension in rural Bangladesh. Further research is important to validate the findings.

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Journal
Archives of Public Health
Published
2026-05-18
DOI
https://doi.org/10.1186/s13690-026-01941-z
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00

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article

Machine learning approaches in identifying factors associated with hypertension and undiagnosed hypertension in adults in rural areas of Bangladesh

Farzana Akhter Bornee, Hasina Akhter Chowdhury, Md. Zahidul Islam, Zarin Raihana et al.
Archives of Public Health
Artificial Intelligence in Healthcare
article

Machine learning approaches in identifying factors associated with hypertension and undiagnosed hypertension in adults in rural areas of Bangladesh

Farzana Akhter Bornee, Hasina Akhter Chowdhury, Md. Zahidul Islam, Zarin Raihana, Mamunur Rashid, Manzur Kader, Mohammad Rocky Khan Chowdhury
article en

Abstract

BACKGROUND: Hypertension is a major cause of death and disability, and undiagnosed cases are particularly dangerous as they can cause severe damage without timely treatment. The aim of the study was to identify risk factors for hypertension and undiagnosed hypertension in rural areas of Bangladesh using advanced Machine Learning (ML) algorithms. METHODS: This study involved 1,603 respondents, selected through a cross-sectional survey using a multistage cluster random sampling technique. Four ML algorithms, including Gradient Booster (GB), Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM), were used in this study. Risk factors for hypertension and undiagnosed hypertension were identified using the best-performing ML model, selected based on metrics such as accuracy, sensitivity, specificity, precision, F1 score, receiver operating characteristics-area under the curve (ROC-AUC), and calibration plot. RESULTS: The prevalence of hypertension was 15.5%, slightly higher than the 15.4% for undiagnosed hypertension. In predicting the risk of both hypertension and undiagnosed hypertension, the LR model outperformed other ML models across most evaluation metrics. For hypertension, it achieved higher performance in terms of precision (0.580), F1 score (0.550), ROC-AUC (0.729; 95% CI: 0.677-0.779), and calibration. Similarly, for undiagnosed hypertension, the LR model showed better precision (0.580), ROC-AUC (0.596; 95% CI: 0.537-0.654), and calibration compared to other models. The risk factors for hypertension and undiagnosed hypertension differed notably. Key risk factors for undiagnosed hypertension included being overweight or obese, the absence of chronic diseases or cardiovascular disease (CVD), being male, non-use of tobacco, older age (above 50 years), being currently married, non-smoking status, having diabetes, and having no formal education. CONCLUSION: The findings emphasize the urgent need for enhanced national and regional public health initiatives to improve the detection and awareness of hypertension in rural Bangladesh. Further research is important to validate the findings.

Archives of Public HealthVol. 84(1)
Jatiya Kabi Kazi Nazrul Islam University (BD), Dalarna University (SE), Bangladesh University (BD), Bangladesh Medical University (BD), University of Gävle (SE), Rajshahi University of Engineering and Technology (BD), Monash University (AU), University of Rajshahi (BD)
Högskolan i Gävle
Quality Education
Openalex Percentile: Top 2%
Artificial Intelligence in Healthcare
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