Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda.

Journal: BMC medical informatics and decision making
PMID: 39939998

Abstract

BACKGROUND: Despite substantial progress in maternal and neonatal health, Rwanda's mortality rates remain high, necessitating innovative approaches to meet health related Sustainable Development Goals (SDGs). By leveraging data collected from Electronic Medical Records, this study explores the application of machine learning models to predict adverse pregnancy outcomes, thereby improving risk assessment and enhancing care delivery.

Authors

  • Muzungu Hirwa Sylvain
    University of Rwanda, Kigali, Rwanda. sylvain.hirwa@yahoo.com.
  • Emmanuel Christian Nyabyenda
    African Center of Excellence in Data Sciences, University of Rwanda, Kigali, Rwanda.
  • Melissa Uwase
    College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda.
  • Isaac Komezusenge
    African Center of Excellence in Data Sciences, University of Rwanda, Kigali, Rwanda.
  • Fauste Ndikumana
    African Center of Excellence in Data Sciences, University of Rwanda, Kigali, Rwanda. nfauste@gmail.com.
  • Innocent Ngaruye
    University of Rwanda, Kigali, Rwanda.

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