BACKGROUND:
Undernutrition is the main cause of
child death in
developing countries. This
paper aimed to explore the
efficacy of
machine learning (ML) approaches in predicting under-five
undernutrition in Ethiopian administrative zones and to identify the most important predictors.
METHOD:
The study employed ML
techniques using retrospective
cross-sectional survey data from
Ethiopia, a national-representative data collected in the year (2000, 2005, 2011, and 2016). We explored six commonly used ML
algorithms;
Logistic regression, Least Absolute Shrinkage and Selection Operator (L-1 regularization
logistic regression), L-2 regularization (
Ridge), Elastic net, neural network, and
random forest (RF).
Sensitivity,
specificity, accuracy, and area under the curve were used to evaluate the performance of those models.
RESULTS:
Based on different performance evaluations, the RF
algorithm was selected as the best ML model. In the order of importance; urban-
rural settlement,
literacy rate of
parents, and place of residence were the major determinants of disparities of
nutritional status for under-five
children among Ethiopian administrative zones.
CONCLUSION:
Our results showed that the considered
machine learning classification algorithms can effectively predict the under-five
undernutrition status in Ethiopian administrative zones. Persistent under-five
undernutrition status was found in the northern part of
Ethiopia. The identification of such high-
risk zones could provide useful information to
decision-makers trying to reduce
child undernutrition.
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