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Development and Validation of Non-Invasive Machine-Learning Screening Models for Pediatric Malnutrition in
Petra Klanjšek1, Petra Povalej Bržan2,3, Nataša Marčun Varda2,4
1Faculty of Health Sciences, University of Maribor, Žitna ulica 15, 2000 Maribor, Slovenia.
Children (Basel, Switzerland)
|May 27, 2026
Summary
New machine learning models effectively screen for child malnutrition using non-invasive data. Simpler models are best for bedside use, aiding early detection of pediatric malnutrition risk.
Area of Science:
- Pediatric Medicine
- Biostatistics
- Computational Biology
Background:
- Child malnutrition is a significant global health issue with severe consequences for growth and development.
- Current screening tools for pediatric malnutrition lack consistent accuracy and practicality.
- Early detection of malnutrition risk is crucial for timely intervention and improved patient outcomes.
Purpose of the Study:
- To develop and validate novel bedside pediatric malnutrition screening models.
- To utilize machine learning and evolutionary computation for complex pattern recognition in non-invasive clinical data.
- To create practical tools for routine ward use in identifying children at risk of malnutrition.
Main Methods:
- A cross-sectional study involving 180 hospitalized children aged 1 month to 18 years.
- Development of screening models using decision trees, random forests, XGBoost, lasso regression, artificial neural networks (ANN), ANFIS, and genetic programming (GP).
- Performance evaluation against the Subjective Global Nutritional Assessment (SGNA) and physician assessment using sensitivity, specificity, AUC, and Cohen's κ.
Main Results:
- Machine learning and intelligent evolutionary models (GP, ANN, ANFIS) demonstrated high diagnostic accuracy (AUC = 0.92-1.00) and agreement (κ = 0.81-1.00) with subjective risk assessment.
- The GP model showed high accuracy but also higher complexity; simpler models like decision trees offered better interpretability and feasibility.
- Validation was limited to a small independent sample without external validation, potentially affecting generalizability.
Conclusions:
- Developed models are non-invasive, cost-effective, and show promise for early malnutrition risk detection upon hospital admission.
- Complex models may function as digital assessment tools, while simpler models are more suitable for rapid bedside screening.
- Further large-scale, multicenter studies are necessary to confirm the generalizability and clinical applicability of these screening models.