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Bridging Data Gaps: Predicting Sub-national Maternal Mortality Rates in Kenya Using Machine Learning Models.

Hellen Muringi Mwaura1, Timothy Kelvin Kamanu2, Benard W Kulohoma3,4

  • 1Department of Biochemistry, University of Nairobi, Nairobi, KEN.

Cureus
|November 27, 2024
PubMed
Summary

Maternal mortality in Kenya is concerning, with a predictive model estimating 367 deaths per 100,000 live births in 2022. This highlights the need for data-driven interventions to improve maternal health outcomes.

Keywords:
demographic and health surveyindicators of maternal mortalitykenyamachine learning modelsmaternal mortalitymaternal mortality ratiosub-national

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Area of Science:

  • Public Health
  • Epidemiology
  • Biostatistics

Background:

  • Maternal mortality remains a critical global health challenge, particularly in sub-Saharan Africa.
  • Kenya faces a disproportionate burden of maternal deaths, risking the achievement of international health targets.
  • Limited reliable maternal health data necessitates innovative predictive modeling.

Purpose of the Study:

  • To develop and apply a predictive model for estimating Kenya's maternal mortality rate (MMR).
  • To analyze sub-national variations in MMR using machine learning techniques.
  • To inform evidence-based interventions and resource allocation for maternal healthcare.

Main Methods:

  • Utilized Demographic and Health Surveys (DHS) data from sub-Saharan African countries.
  • Developed a multiple linear regression model using supervised machine learning in R.
  • Applied the model to predict county-level MMR in Kenya using 2022 KDHS data.

Main Results:

  • Significant correlations found between MMR and total fertility, maternal age at first birth, postnatal clinic attendance, thinness prevalence, and physical violence.
  • The model estimated Kenya's national MMR at 367 deaths per 100,000 live births in 2022.
  • County-level MMR varied significantly, from 49 in Kisii to 1794 in Turkana.

Conclusions:

  • Kenya's MMR shows a slight increase in 2022 compared to 2019 estimates, potentially linked to COVID-19.
  • Predictive modeling offers a valuable tool to complement existing data systems for maternal health.
  • Integrating predictive models is crucial for enhancing maternal healthcare and resource allocation in Kenya.