Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.

ABSTRACT:

Background

Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition status during adolescence, in addition to infancy and childhood is very crucial. However, the available studies in Ethiopia have been usually focused in early childhood and they used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.

Methods

A total of 3156 weighted samples of adolescent girls aged 15-19 years were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 80% and 20% of the observations were used for training,

SUBMITTER: Zemariam AB 

PROVIDER: S-EPMC11760002 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature