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

• Published in: AMIA ... Annual Symposium proceedings Vol. 2020; pp. 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: Journal Article
• Language: English
• Published: United States American Medical Informatics Association 2020
• Subjects: Africa South of the Sahara - epidemiology; Female; Humans; Infant; Infant Mortality; Infant, Newborn; Machine Learning; Male; Mothers; Surveys and Questionnaires; Africa South of the Sahara
• ISSN: 1559-4076

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.