Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia

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. This study draws on data from the Ethiopian Demographic and Health Survey o...

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Bibliographic Details
Published in:	Public health nutrition Vol. 25; no. 2; pp. 269 - 280
Main Authors:	Bitew, Fikrewold H, Sparks, Corey S, Nyarko, Samuel H
Format:	Journal Article
Language:	English
Published:	Cambridge, UK Cambridge University Press 01.02.2022
Subjects:	Age Algorithms Anemia Artificial intelligence birth weight Child Child, Preschool childhood Children Children & youth Classification Demographics Discriminant analysis Ethiopia Ethiopia - epidemiology Fertility Food security Growth Disorders - etiology Health surveys Households Humans Infant Learning algorithms Machine Learning Malnutrition Malnutrition - complications Malnutrition - diagnosis Malnutrition - epidemiology Maternal & child health Neural networks Nutrition Nutritional status Nutritional Status and Body Composition prediction Predictions Prevalence Public health Research Paper Risk analysis Risk factors Thinness - epidemiology Thinness - etiology Undernutrition Underweight Variables Water supply Ethiopia Africa Determinants Predictive algorithms Child undernutrition Spatial variations Ethiopia Machine learning
ISSN:	1368-9800, 1475-2727, 1475-2727
Online Access:	Get full text
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