Prediction of undernutrition and identification of its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.

ABSTRACT:

Background and objectives

Child undernutrition is a leading global health concern, especially in low and middle-income developing countries, including Bangladesh. Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.

Materials and methods

This study used the latest nationally representative cross-sectional Bangladesh demographic health survey (BDHS), 2017-18 data. The Boruta technique was implemented to identify the important predictors of undernutrition, and logistic regression, artificial neural network, random forest, and extreme gradient boosting (XGB) were adopted to predict undernutriti

SUBMITTER: Islam MM 

PROVIDER: S-EPMC11623790 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature