Predictors of undiagnosed diabetes in the Bangladeshi female population: a propensity score–weighted machine learning analysis of BDHS biomarker data

Background In Bangladesh, diabetes mellitus has become a substantial public health burden, imposing a strain on the population both economically and clinically. Robust methods for identifying high-risk populations using national data are urgently needed due to the increasing prevalence of metabolic noncommunicable illnesses throughout South Asia. Using data from the most recent wave of a reliable national survey, this study was aimed at creating and testing a diabetes prediction model for adult female Bangladeshis.

Methods The 2022 Bangladesh Demographic and Health Survey (BDHS) biomarker data were examined, where key factors of undiagnosed diabetes were identified using stacked ensemble machine learning (ML) algorithms together with explainable AI (XAI) approaches after adjusting for selection bias using a propensity score model. A nomogram, a user-friendly clinical tool for risk assessment, was created. The area under the receiver operating characteristic (AUROC) curve was used to assess the model′s performance.

Results Of the 18,547 weighted female participants (unweighted n = 7833), the prevalence of undiagnosed diabetes was 6.26% (95% CI: 5.52%–7.00%). Significant predictors identified included age (mean 42 ± 16 years vs. 38 ± 16 years; p < 0.001), BMI (mean 24.7 ± 4.6 kg/m2; p < 0.001), and hypertension (mean 120/78 mmHg; p = 0.001). ML models demonstrated high predictive accuracy (AUROC > 0.80), and a simplified clinical nomogram was developed to provide personalized risk scores. XAI (SHAP) analysis emphasized nonlinear influences, particularly in urban residents, who accounted for 40% of undiagnosed cases, and the richest quintile, which bore a disproportionate burden of 33% (p < 0.001) compared to just 13% in the poorest quintile.

Conclusion For early diabetes screening in Bangladesh, the established predictive model and nomogram provide an evidence-based method. These technologies can help healthcare providers and policymakers tailor interventions toward high-risk groups by using nationally representative data. This could potentially lessen the sustained health and financial effects of the diabetes epidemic.