Artificial and Convolutional Neural Network Architectures for Childhood Stunting Classification: Design, Evaluation, and Optimization

Childhood stunting is a global health challenge, affecting 148 million children under 5 in 2022. It is a key indicator of chronic malnutrition, often driven by inadequate nutrition, recurrent infections, and socio-economic disparities. In Egypt, 22% of children under 5 are stunted, leading to cognit...

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Vydané v:SAGE open Ročník 15; číslo 3
Hlavní autori: Hendy, Abdelaziz, Ibrahim, Rasha Kadri, Abdelaliem, Sally Mohammed Farghaly, Alenezi, Ibrahim Naif, Sinnokrot, Shaban Majed, Aldosari, Nasser, Alshammari, Afrah Madyan, Hendy, Ahmed
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Los Angeles, CA SAGE Publications 01.09.2025
SAGE PUBLICATIONS, INC
SAGE Publishing
Predmet:
Accuracy
Body height
Child development
Child Health
Childhood
Children
Childrens health
Classification
Dropping out
Early intervention
Educational Objectives
Health care industry
Health services
Health status
Health surveys
Interdisciplinary aspects
Learning outcomes
Machine learning
Malnutrition
National security
Networks
Neural networks
Nutrition
Optimization
Outcomes of Education
Outcomes of Treatment
Recurrent
Resource allocation
Socioeconomic factors
Egypt
artificial neural networks
autoencoders
child nutrition disorders
convolutional neural networks
stunting
Egypt
ISSN:2158-2440, 2158-2440
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Shrnutí:Childhood stunting is a global health challenge, affecting 148 million children under 5 in 2022. It is a key indicator of chronic malnutrition, often driven by inadequate nutrition, recurrent infections, and socio-economic disparities. In Egypt, 22% of children under 5 are stunted, leading to cognitive delays, poor educational outcomes, and long-term economic losses. Innovative and interdisciplinary approaches are essential to address this issue. This research aims to enhance the detection of growth abnormalities in children using advanced machine learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Autoencoder-based architectures. The study utilized secondary data from Demographic Health Surveys (DHS) conducted in Egypt between 2005 and 2014, comprising 37,051 records. Key maternal and child characteristics were analyzed to calculate Height-for-Age Z-scores (HAZ) and Weight-for-Age Z-scores (WAZ). A 70:30 train-test split was applied, and dropout layers were used to prevent overfitting during model training. The ANN model achieved 99.5% accuracy, with a precision of 97.2% for normal cases and 95.4% for severely stunted cases. The CNN model achieved lower accuracy (68%) but provided valuable insights into spatial growth patterns. Autoencoder-enhanced models (e.g., AE + ANN) demonstrated moderate performance, with AE + ANN achieving 77.2% accuracy. Misclassification rates for stunted versus severely stunted cases reached up to 14%. This study demonstrates the potential of machine learning models in early detection and intervention for childhood stunting. By leveraging automated, data-driven approaches, healthcare providers can make evidence-based decisions, allocate resources effectively, and improve child health outcomes in Egypt and beyond. Graphical Abstract
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ISSN:2158-2440
2158-2440
DOI:10.1177/21582440251365428