Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia
Date
2021-10-08Author
Bitew, Fikrewold H.
Sparks, Corey S.
Nyarko, Samuel H.
Metadata
Show full item recordAbstract
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
stunting, wasting and underweight in Ethiopia. Also, among the five ML algorithms,
xgbTree algorithm shows a better prediction ability than the generalised linear
mixed algorithm. The best predicting algorithm (xgbTree) shows diverse important
predictors of undernutrition across the three outcomes which include time to water
source, anaemia history, child age greater than 30 months, small birth size and
maternal underweight, among others.
Conclusions: The xgbTree algorithm was a reasonably superior ML algorithm
for predicting childhood undernutrition in Ethiopia compared to other ML
algorithms considered in this study. The findings support improvement in access
to water supply, food security and fertility regulation, among others, in the quest to
considerably improve childhood nutrition in Ethiopia.
Department
Demography
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