Random forest algorithm for predicting tobacco use and identifying determinants among pregnant women in 26 sub-Saharan African countries: a 2024 analysis.

Journal: BMC public health
PMID: 40269837

Abstract

INTRODUCTION: Tobacco use during pregnancy is a significant public health concern, associated with adverse maternal and neonatal outcomes. Despite its critical importance, comprehensive data on tobacco use among pregnant women in sub-Saharan Africa is limited. Leveraging machine learning approaches allows us to better understand these constraints and predict tobacco use among pregnant women, providing actionable insights for policy and intervention.

Authors

  • Eliyas Addisu Taye
    Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
  • Eden Yitbarek Woubet
    Department of Reproductive Health, Institute of Public Health, University of Gondar, Gondar, Ethiopia.
  • Gabrela Yimer Hailie
    Department of Environmental and Occupational Health and Safety, Institute of Public Health, University of Gondar, Gondar, Ethiopia.
  • Adem Tsegaw Zegeye
    Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia. ademtsegaw0594@gmail.com.
  • Fetlework Gubena Arage
    Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
  • Tigabu Eskeziya Zerihun
    Department of Clinical Pharmacy, Pharmacy Education and Clinical Services Directorate, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
  • Abel Temeche Kassaw
    Department of Clinical Pharmacy, Pharmacy Education and Clinical Services Directorate, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.

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