Machine learning algorithms to predict feeding practices during diarrheal disease and its determinants among under-five children in East Africa

Bibliographic Data

ID22089174
AuthorsTirualem Zeleke Yehuala (0009-0007-3909-8825, University of Gondar, corresponding author), Nebebe Demis Baykemagn (0009-0008-9403-6915, University of Gondar), Bewuketu Terefe (0000-0002-0063-0999, University of Gondar, corresponding author)
Year2025
Volume13
Pages1513922-1513922
Publication date2025-07-23
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1513922
PMID40777656
OpenAlexW4412583713
LanguageEN
References cited39

Background: Diarrhea is the leading cause of childhood malnutrition. Although replacement, continued feeding, and increasing appropriate fluid at home during diarrhea episodes are the cornerstones of treatment packages, food and fluid restrictions are common during diarrheal illnesses in Africa. To fill the methodological and current evidence gaps, this study aimed to build models and predict determinants to increase feeding practices of children in East Africa during diarrheal outbreaks. Methods: We used the most recent demographic and health survey (DHS) statistics from 12 East African nations collected between 2012 and 2023. The analyses included a total weighted sample of 20,059 children aged 5 years. Python software was utilized for data processing and machine learning model building. We employed four ML algorithms, such as Random Forest (RF), Decision Tree (DT), XGB (Extreme Gradient Boosting), and Logistic Regression (LR). In this work, we evaluated the predictive models' performance using performance assessment criteria such as accuracy, precision, recall, and the AUC curve. Results: In this study, 20,059 children aged 5 years were used in the final analysis. Among the proposed machine learning models, random forest performed best overall in the proposed classifier, with an accuracy of 97.86%, precision of 98%, recall of 77%, F-measure of 86%, and AUC curve of 97%. The most significant determinants of increasing feeding practice were richest household, faculty delivery, use of modern contraception method, the number of children 3-5, women's employment status, maternal age is 25-34, having media exposure, and health-seeking decisions made by mothers were associated positively, whereas not using contraception, home delivery, the total number of children is large, and the sex of the household was male, which was associated negatively with feeding practice during diarrhea in East Africa. Conclusion: Machine learning (ML) algorithms have provided valuable insights into the complex factors influencing feeding practices during diarrheal disease in under-five children in East Africa. During diarrhea, only 11 of the 100 children received acceptable child feeding practices. More than one-third of the patients received less than usual or nothing. Reducing diarrhea-related child mortality by improving diarrhea management practices is recommended, particularly focusing on the identified aspects

Decision tree · Diarrhea · Environmental health · Logistic regression · Machine learning · Malnutrition · Random forest · Child Nutrition and Water Access · Computer Science · Demography · Food Security and Health in Diverse Populations · Global Maternal and Child Health · Medicine · Artificial Intelligence · Pediatrics

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