Evidence map›Paper›PMID 41673128›Full record

ArticleScientific reports2026

Machine learning based identification of key predictors and socioeconomic inequalities in the co-existence of diabetes and hypertension among Bangladeshi adults.

Rafayet Rahman Ridoy, Md Mothashin, Md Aslam Hossain, Tahira Mahbub, Sajal Chandra Barmon, Parth Kumar Saha, Abu Sayed Md Al Mamun, Md Golam Hossain

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Article in Scientific reports, 2026. 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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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Rafayet Rahman RidoyHealth Research Group, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md MothashinHealth Research Group, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md Aslam HossainHealth Research Group, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Tahira MahbubInternational University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.
Sajal Chandra BarmonHealth Research Group, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Parth Kumar SahaHealth Research Group, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Abu Sayed Md Al MamunHealth Research Group, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Md Golam HossainHealth Research Group, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh. hossain95@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Type 2 diabetes mellitus and hypertension often coexist, posing significant public health challenges worldwide. This study aims to identify the associated factors of coexistence of hypertension and diabetes (CHD) among adults in Bangladesh using machine learning (ML) algorithms to rank these predictors according to their relative influence. This was a cross sectional household study. The secondary data was extracted from nationally representative dataset collected by Bangladesh Demographic and Health Survey (BDHS), 2022. We analyzed Bangladeshi 13,541 adults (6,211 males; 7,330 females) aged ≥ 18 years. Chi-square tests and logistic regression identified associated factors, while XGBoost with SHAP quantified feature contributions. Inequalities were assessed using concentration curves (CC), concentration index (CCI), and decomposition analysis. Prevalence was 28.5% for hypertension, 16.6% for diabetes, and 6.9% for CHD. Higher risk of CHD was linked to Chattogram, Dhaka, Sylhet divisions, female sex, urban residence, hygienic toilet use, college education, higher wealth, malnutrition (over/underweight), age ≥ 35 years, and unsafe water use (p < 0.05). SHAP ranked older age as the strongest predictor, followed by BMI, wealth, and region; larger family size was protective. The CC lay below equality (CCI = 0.2651, p < 0.001), showing disproportionate prevalence among the affluent. Wealth (11.7%), BMI (6.1%), region (2.7%), education (2.0%), and residence (1.3%) were key contributors to inequality. CHD in Bangladesh is concentrated among wealthier adults, largely driven by older age, high BMI, and socioeconomic disparities. Addressing these determinants is crucial to reduce the burden and inequality of these conditions.

Indexed as

Diabetes Mellitus, Type 2HypertensionMachine LearningAdolescentAdultAgedBangladeshBoosting Machine Learning AlgorithmsCross-Sectional StudiesFemaleHumansMaleMiddle AgedPredictive Learning ModelsPrevalenceRisk FactorsCoexistingDiabetesHypertensionMachine learning, decomposition analysis

Identifiers

PMID41673128
PMCPMC12963369

What Socratic holds

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.