Predicting neonatal mortality using ensemble machine learning algorithms in the case of Ethiopian Rural Areas

Predicting neonatal mortality using ensemble machine learning algorithms in the case of Ethiopian Rural Areas

Abstract Background Each year, approximately 2.5 million newborns die globally, with developing countries bearing the impact of this crisis. Sub-Saharan Africa has the highest neonatal mortality rate, with Ethiopia facing alarmingly high figures, particularly in rural areas where mortality is signif...

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Bibliographic Details
Main Authors: Melaku Alelign Mengstie, Misganaw Telake Telele
Format: Article
Language:English
Published: Springer 2025-08-01
Series:Discover Artificial Intelligence
Subjects:
Ensemble algorithms
Healthcare inequity
Maternal health
Neonatal mortality
Rural health disparities
SHAP
Online Access:https://doi.org/10.1007/s44163-025-00305-w
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https://doi.org/10.1007/s44163-025-00305-w

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