Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey.
<h4>Background</h4>Hypertension poses a significant public health challenge in low- and middle-income countries. In Bangladesh, the Health Population and Nutrition Sector Development Program has shown effectiveness in resource-limited settings. Estimating causal relationships on hyperten...
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Public Library of Science (PLoS)
2025-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1013211 |
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| author | Probir Kumar Ghosh Md Aminul Islam Md Ahshanul Haque Md Tariqujjaman Novel Chandra Das Mohammad Ali Md Rasel Uddin Md Golam Dostogir Harun |
| author_facet | Probir Kumar Ghosh Md Aminul Islam Md Ahshanul Haque Md Tariqujjaman Novel Chandra Das Mohammad Ali Md Rasel Uddin Md Golam Dostogir Harun |
| author_sort | Probir Kumar Ghosh |
| collection | DOAJ |
| description | <h4>Background</h4>Hypertension poses a significant public health challenge in low- and middle-income countries. In Bangladesh, the Health Population and Nutrition Sector Development Program has shown effectiveness in resource-limited settings. Estimating causal relationships on hypertension while adjusting for nonlinear observed confounders in adult population is complex. This study aims to identify predictors of hypertension, and explore observational causal inference on hypertension.<h4>Methods</h4>The hypertension data was analyzed using Bangladesh Demographic and Health surveys data from the 2011 and 2022. We used 11,815 individuals aged 34 years and above. Hypertension was defined as a systolic blood pressure of > 140 mm Hg and/or a diastolic blood pressure of > 90 mm Hg and/or having a history of hypertension. We used logistic regression, Random forest model, Double Machine Learning (DML), and Shapley Additive exPlanations (SHAP) based on a pre-defined causal structure.<h4>Results</h4>The dataset included 11,815 individuals, and the prevalence of hypertension was 38.40%. The average age of individuals was 52.76 years (SD: 12.97), and 6826 (58.77%) were male. The Random forest model achieved 93% accuracy, with evaluation f1-scores of 95% for non-hypertension and 91% for hypertension, and identified older age, female gender, urban residency, workers, wealthier, self-awareness, and excessive body weight as key predictors of hypertension. The individual conditional expectation and SHAP plots reveal that age, and body mass index (BMI) are nonlinear relation with hypertension. The crude OR between excessive body weight and hypertension was 2.24 (95%CI: 2.07 - 2.42). Adjusted for age, sex, socioeconomic status (SES), and self-awareness, the OR was 1.97 (95%CI: 1.79 - 2.17), and using de-biased method, it was 1.30 (95%CI: 1.17 - 1.43).<h4>Conclusion</h4>The study highlights important predictors of hypertension, including age, sex, residency, and socioeconomic status (SES), self-awareness and body weight. The machine learning model achieved an accuracy of 93% in predicting hypertension. The de-biased methods provided a more refined risk estimate. Age and excessive body weight were found to significantly contributed to hypertension, demonstrating complex interactions and varying marginal effects across different levels of these factors. Awareness programs and targeted interventions are vital to effectively reduce excessive body weight and prevent hypertension. |
| format | Article |
| id | doaj-art-7d6a7a6df26543f8b3c0b0b69461681c |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Public Library of Science (PLoS) |
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| spelling | doaj-art-7d6a7a6df26543f8b3c0b0b69461681c2025-08-23T05:31:13ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-07-01217e101321110.1371/journal.pcbi.1013211Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey.Probir Kumar GhoshMd Aminul IslamMd Ahshanul HaqueMd TariqujjamanNovel Chandra DasMohammad AliMd Rasel UddinMd Golam Dostogir Harun<h4>Background</h4>Hypertension poses a significant public health challenge in low- and middle-income countries. In Bangladesh, the Health Population and Nutrition Sector Development Program has shown effectiveness in resource-limited settings. Estimating causal relationships on hypertension while adjusting for nonlinear observed confounders in adult population is complex. This study aims to identify predictors of hypertension, and explore observational causal inference on hypertension.<h4>Methods</h4>The hypertension data was analyzed using Bangladesh Demographic and Health surveys data from the 2011 and 2022. We used 11,815 individuals aged 34 years and above. Hypertension was defined as a systolic blood pressure of > 140 mm Hg and/or a diastolic blood pressure of > 90 mm Hg and/or having a history of hypertension. We used logistic regression, Random forest model, Double Machine Learning (DML), and Shapley Additive exPlanations (SHAP) based on a pre-defined causal structure.<h4>Results</h4>The dataset included 11,815 individuals, and the prevalence of hypertension was 38.40%. The average age of individuals was 52.76 years (SD: 12.97), and 6826 (58.77%) were male. The Random forest model achieved 93% accuracy, with evaluation f1-scores of 95% for non-hypertension and 91% for hypertension, and identified older age, female gender, urban residency, workers, wealthier, self-awareness, and excessive body weight as key predictors of hypertension. The individual conditional expectation and SHAP plots reveal that age, and body mass index (BMI) are nonlinear relation with hypertension. The crude OR between excessive body weight and hypertension was 2.24 (95%CI: 2.07 - 2.42). Adjusted for age, sex, socioeconomic status (SES), and self-awareness, the OR was 1.97 (95%CI: 1.79 - 2.17), and using de-biased method, it was 1.30 (95%CI: 1.17 - 1.43).<h4>Conclusion</h4>The study highlights important predictors of hypertension, including age, sex, residency, and socioeconomic status (SES), self-awareness and body weight. The machine learning model achieved an accuracy of 93% in predicting hypertension. The de-biased methods provided a more refined risk estimate. Age and excessive body weight were found to significantly contributed to hypertension, demonstrating complex interactions and varying marginal effects across different levels of these factors. Awareness programs and targeted interventions are vital to effectively reduce excessive body weight and prevent hypertension.https://doi.org/10.1371/journal.pcbi.1013211 |
| spellingShingle | Probir Kumar Ghosh Md Aminul Islam Md Ahshanul Haque Md Tariqujjaman Novel Chandra Das Mohammad Ali Md Rasel Uddin Md Golam Dostogir Harun Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey. PLoS Computational Biology |
| title | Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey. |
| title_full | Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey. |
| title_fullStr | Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey. |
| title_full_unstemmed | Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey. |
| title_short | Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey. |
| title_sort | identifying predictors and assessing causal effect on hypertension risk among adults using double machine learning models insights from bangladesh demographic and health survey |
| url | https://doi.org/10.1371/journal.pcbi.1013211 |
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