Beyond health: A machine learning analysis of structural barriers to school attainment in Somalia.

ABSTRACT: In fragile states like Somalia, the link between poor health and educational exclusion is critical yet poorly understood. This study uses a novel machine learning approach to identify and rank the most significant barriers to school attendance. We analyzed nationally representative data from 10,511 children aged 6-18 in the 2022 Somalia Integrated Household Budget Survey (SIHBS). Ten supervised machine learning models were employed to predict school attendance, with the Random Forest model emerging as the top performer (AUC = 0.86). Contrary to conventional wisdom, our findings reveal a clear hierarchy of barriers where structural and demographic factors are the most powerful predictors. A child's age (non-attendance rises from 6% in 6-10 year-olds to 25% in 15-18 year-olds), geographic re

SUBMITTER: Ali JA 

PROVIDER: S-EPMC12530525 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature