Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey.

Journal: PLoS computational biology
Published Date: Jul 2, 2025

Abstract

BACKGROUND: 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.

Authors

  • Probir Kumar Ghosh
    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
  • Md Aminul Islam
    COVID-19 Diagnostic Lab, Department of Microbiology, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh; Advanced Molecular Lab, Department of Microbiology, President Abdul Hamid Medical College, Karimganj, Kishoreganj, Bangladesh.
  • Md Ahshanul Haque
    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
  • Md Tariqujjaman
    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
  • Novel Chandra Das
    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
  • Mohammad Ali
    Center for Biotechnology and Microbiology, University of Swat, Pakistan.
  • Md Rasel Uddin
    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
  • Md Golam Dostogir Harun
    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.

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