A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones.

ABSTRACT:

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 evalua

SUBMITTER: Fenta HM 

PROVIDER: S-EPMC8542294 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature