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February 13, 2026Scientific Reports0 citationsOpen Access

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

RRRafayet Rahman RidoyInternational University of Business Agriculture and Technology MMMd. MothashinUniversity of Rajshahi MHMd. Aslam Hossain

Key Points

To identify associated factors and inequalities in the coexistence of diabetes and hypertension among adults in Bangladesh using machine learning.
Cross-sectional household study utilizing secondary data from Bangladesh Demographic and Health Survey 2022
Analysis of 13,541 adults aged ≥ 18 years
Chi-square tests and logistic regression for factor identification
Machine learning with XGBoost and SHAP for ranking predictors
Concentration curves and decomposition analysis for inequality assessment.
Prevalence was 28.5% for hypertension and 16.6% for diabetes in the study population
Older age, high BMI, and socioeconomic disparities were key determinants of coexistence
Inequalities concentrated among wealthier individuals (CCI = 0.2651, p < 0.001)
Key contributors to inequality included wealth, BMI, region, education, and urban residence.

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.

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