{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T08:10:05Z","timestamp":1787731805845,"version":"build-2784847793"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1013211","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000}}],"reference-count":63,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:sec id=\"sec014\">\n<jats:title>Background<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec015\">\n<jats:title>Methods<\/jats:title>\n<jats:p>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 &gt; 140\u2009mm Hg and\/or a diastolic blood pressure of &gt; 90\u2009mm 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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec016\">\n<jats:title>Results<\/jats:title>\n<jats:p>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 \u2013 2.42). Adjusted for age, sex, socioeconomic status (SES), and self-awareness, the OR was 1.97 (95%CI: 1.79 \u2013 2.17), and using de-biased method, it was 1.30 (95%CI: 1.17 \u2013 1.43).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec017\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pcbi.1013211","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T14:00:07Z","timestamp":1751464807000},"page":"e1013211","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1580-5883","authenticated-orcid":true,"given":"Probir Kumar","family":"Ghosh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. 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