Identification of factors associated with acute malnutrition in children under 5 years and forecasting future prevalence: assessing the potential of statistical and machine learning methods.

ABSTRACT:

Introduction

Eliminating acute malnutrition in children under 5 years of age stands as a critical health priority outlined in the United Nations Sustainable Development Goal 2, 'Zero Hunger'. This requires targeted provision of treatment and preventative services. However, accurately forecasting future prevalence of cases remains challenging, with the application of predictive models being notably scarce. Addressing this gap, this paper aims to identify factors associated with Global Acute Malnutrition (GAM) and explores the potential of machine learning in predicting its prevalence using data from Somalia.

Methods

Survey data on GAM prevalence systematically collected in Somalia every 6 months at a district level from 2017 to 2021 were collated alongside a range of potentia

SUBMITTER: Reusken M 

PROVIDER: S-EPMC11883882 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature