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Peer-reviewedCC BY 4.0PLOS One

Machine learning and deep learning–based prediction of hypertension and analysis of its major risk factors in Bangladesh

  • Shawrab Chandra1
  • Md. Maeen Molla2
  • Samiul Islam3
  • Md. Matiur Rahaman4
  • Mohammad Ali5
  • Md. Ayub Ali
5 affiliations
  1. 1Department of Statistics and Data Science, University of Barishal, Barishal, Bangladesh
  2. 2Department of Statistics, Pirojpur Science and Technology University, Pirojpur, Bangladesh
  3. 3Department of Statistics and Data Science, University of Rajshahi, Rajshahi, Bangladesh
  4. 4Department of Statistics, Faculty of Science, Gopalganj Science and Technology University, Gopalganj, Bangladesh
  5. 5Statistics Discipline, Khulna University, Khulna, Bangladesh
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Abstract

Background: Hypertension is a leading cause of cardiovascular morbidity and mortality in Bangladesh. This study examined its prevalence, risk factors, and predictive modeling using machine learning (ML) and deep learning (DL) approaches. Method: We analyzed cross-sectional data from the 2022 Bangladesh Demographic and Health Survey, which included 14,283 adults (≥18 years). Prevalence was estimated, chi-square tests assessed associations, and four ML models (weighted logistic regression, random forest, extreme gradient boosting, light gradient boosting machine) and two DL models (TabNet, and multi-layer perceptron) were applied to predict hypertension risk. Model performance was evaluated using accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristics curve and precision-recall curve. Results: Overall prevalence was 18.04% (95% CI: 17.2%–18.9%), higher among women (18.87%) than men (16.97%). The chi-square test suggests that hypertension was significantly associated with age, BMI, diabetes, wealth index, education, household size, and region (p < 0.05). Among the machine learning and deep learning models, weighted logistic regression (WLR) achieved the highest accuracy (0.817), precision (0.444), specificity (0.981), AUC-ROC (0.751), and AUC-PR (0.357). However, WLR exhibited low recall (0.070). In contrast, the random forest (RF) model achieved the highest recall (0.687) and F1-score (0.460) on the test data, indicating greater sensitivity in identifying individuals with hypertension. Additionally, age, BMI, sex, family size, and educational level were identified as the most important predictors among the variables included in the study. Conclusion: Hypertension is common in Bangladesh, with higher prevalence in women and significant association with socio-demographic determinants. Although WLR demonstrated the highest accuracy, precision, specificity, and AUC-PR, its low recall limits its utility for identifying individuals with hypertension. RF may be more suitable for public health applications because of its higher recall and F1-score; however, further external validation and assessment of its clinical utility are required before implementation.

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Cite

BibTeXRIS
BibTeX
@article{chandra2026machine,
  title = {{Machine learning and deep learning–based prediction of hypertension and analysis of its major risk factors in Bangladesh}},
  author = {Shawrab Chandra and Md. Maeen Molla and Samiul Islam and Md. Matiur Rahaman and Mohammad Ali and Md. Ayub Ali},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0358471},
  url = {https://doi.org/10.1371/journal.pone.0358471},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Machine learning and deep learning–based prediction of hypertension and analysis of its major risk factors in Bangladesh
AU  - Shawrab Chandra
AU  - Md. Maeen Molla
AU  - Samiul Islam
AU  - Md. Matiur Rahaman
AU  - Mohammad Ali
AU  - Md. Ayub Ali
PY  - 2026
DA  - 2026/09/17
JO  - PLOS One
DO  - 10.1371/journal.pone.0358471
UR  - https://doi.org/10.1371/journal.pone.0358471
AB  - Background: Hypertension is a leading cause of cardiovascular morbidity and mortality in Bangladesh. This study examined its prevalence, risk factors, and predictive modeling using machine learning (ML) and deep learning (DL) approaches. Method: We analyzed cross-sectional data from the 2022 Bangladesh Demographic and Health Survey, which included 14,283 adults (≥18 years). Prevalence was estimated, chi-square tests assessed associations, and four ML models (weighted logistic regression, random forest, extreme gradient boosting, light gradient boosting machine) and two DL models (TabNet, and multi-layer perceptron) were applied to predict hypertension risk. Model performance was evaluated using accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristics curve and precision-recall curve. Results: Overall prevalence was 18.04% (95% CI: 17.2%–18.9%), higher among women (18.87%) than men (16.97%). The chi-square test suggests that hypertension was significantly associated with age, BMI, diabetes, wealth index, education, household size, and region (p < 0.05). Among the machine learning and deep learning models, weighted logistic regression (WLR) achieved the highest accuracy (0.817), precision (0.444), specificity (0.981), AUC-ROC (0.751), and AUC-PR (0.357). However, WLR exhibited low recall (0.070). In contrast, the random forest (RF) model achieved the highest recall (0.687) and F1-score (0.460) on the test data, indicating greater sensitivity in identifying individuals with hypertension. Additionally, age, BMI, sex, family size, and educational level were identified as the most important predictors among the variables included in the study. Conclusion: Hypertension is common in Bangladesh, with higher prevalence in women and significant association with socio-demographic determinants. Although WLR demonstrated the highest accuracy, precision, specificity, and AUC-PR, its low recall limits its utility for identifying individuals with hypertension. RF may be more suitable for public health applications because of its higher recall and F1-score; however, further external validation and assessment of its clinical utility are required before implementation.
SN  - 1932-6203
ER  -