Predicting and identifying factors associated with undernutrition among children under five years in Ghana using machine learning algorithms.

ABSTRACT:

Background

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors.

Methods

Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extrem

SUBMITTER: Anku EK 

PROVIDER: S-EPMC10863846 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature