Identifying Factors Associated with Neonatal Mortality in Sub-Saharan Africa using Machine Learning

This study aimed at identifying the factors associated with neonatal mortality. We analyzed the Demographic and Health Survey (DHS) datasets from 10 Sub-Saharan countries. For each survey, we trained machine learning models to identify women who had experienced a neonatal death within the 5 years pr...

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Bibliographic Details
Published in:AMIA ... Annual Symposium proceedings 2020, Vol.2020, p.963-972
Main Authors: Ogallo, William, Speakman, Skyler, Akinwande, Victor, Varshney, Kush R, Walcott-Bryant, Aisha, Wayua, Charity, Weldemariam, Komminist, Mershon, Claire-Helene, Orobaton, Nosa
Format: Article
Language:English
Subjects:
Africa South of the Sahara - epidemiology
Female
Humans
Infant
Infant Mortality
Infant, Newborn
Machine Learning
Male
Mothers
Surveys and Questionnaires
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Summary:This study aimed at identifying the factors associated with neonatal mortality. We analyzed the Demographic and Health Survey (DHS) datasets from 10 Sub-Saharan countries. For each survey, we trained machine learning models to identify women who had experienced a neonatal death within the 5 years prior to the survey being administered. We then inspected the models by visualizing the features that were important for each model, and how, on average, changing the values of the features affected the risk of neonatal mortality. We confirmed the known positive correlation between birth frequency and neonatal mortality and identified an unexpected negative correlation between household size and neonatal mortality. We further established that mothers living in smaller households have a higher risk of neonatal mortality compared to mothers living in larger households; and that factors such as the age and gender of the head of the household may influence the association between household size and neonatal mortality.
ISSN:1559-4076