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

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.