Understanding drivers of neonatal mortality in Zimbabwe: A machine learning approach using survey data.

ABSTRACT: In Zimbabwe, the neonatal mortality rate (NMR) is higher than the regional average, and the country is not on track to reach the Sustainable Development Goal of reducing the NMR by 2030. While other child mortality indicators have improved, NMR has increased. Using machine learning, we aimed to identify the key predictors of neonatal mortality in Zimbabwe. Pooled secondary data analysis of three rounds of the Zimbabwe Demographic Health Survey (ZDHS) from 2005 to 2015 was done. The study population was all the live births born to women aged 15-49 years within the 5 years prior to each round of the survey (n = 16,941). Multiple supervised binary classification machine learning models were built to predict neonatal death based on socio-economic, mother's demographic, prenatal, delivery, and

SUBMITTER: Mbinda A 

PROVIDER: S-EPMC12854431 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature