Dynamic Bayesian network modeling for longitudinal data on child undernutrition in Ethiopia (2002-2016).

ABSTRACT:

Introduction

Dynamic Bayesian networks improve the modeling of complex systems by incorporating continuous probabilistic relationships between covariates that change over time. This study aimed to analyze the complex causal links contributing to child undernutrition using dynamic Bayesian network modeling, examining both the best- and worst-case scenarios. The Young Cohort of the Ethiopian Young Lives dataset from 2002-2016 was used to analyze the complex relationships among various covariates influencing child undernutrition. We used a built-in Bayes server tool to identify potential features, followed by building the structure of the directed acyclic graph using a structural learning algorithm. The maximum posterior is determined using the relevance tree algorithm. The node with

SUBMITTER: Begashaw GB 

PROVIDER: S-EPMC11669556 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature