Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia.

ABSTRACT:

Objective

Child undernutrition is a global public health problem with serious implications. In this study, we estimate predictive algorithms for the determinants of childhood stunting by using various machine learning (ML) algorithms.

Design

This study draws on data from the Ethiopian Demographic and Health Survey of 2016. Five ML algorithms including eXtreme gradient boosting, k-nearest neighbours (k-NN), random forest, neural network and the generalised linear models were considered to predict the socio-demographic risk factors for undernutrition in Ethiopia.

Setting

Households in Ethiopia.

Participants

A total of 9471 children below 5 years of age participated in this study.

Results

The descriptive results show substantial regional variations in child

SUBMITTER: Bitew FH 

PROVIDER: S-EPMC8883776 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature