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Deep Neural Networks for Image-Based Dietary Assessment
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Machine learning models for predicting malnutrition in NICU patients: A comprehensive benchmarking study.

Sander M W Janssen1, Yamine Bouzembrak2, Nadir Yalcin3

  • 1Information Technology Group, Wageningen University and Research, Wageningen, the Netherlands; Department of Primary and Community Care, Radboud Institute for Health Sciences, Radboudumc, Nijmegen, the Netherlands.

Computers in Biology and Medicine
|May 8, 2025
PubMed
Summary

Machine learning models offer efficient alternatives for malnutrition screening. Generalized Linear Models with Lasso or Elastic Net Regularization (GLMnet) and Extreme Gradient Boosting (XGBoost) showed promising results for nutritional assessment.

Keywords:
Decision supportMachine learningMalnutritionNutritional assessmentNutritional screening toolPrecision nutrition

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Area of Science:

  • Nutritional Science
  • Computational Biology
  • Medical Informatics

Background:

  • Malnutrition is a global health issue impacting adults and children, stemming from insufficient nutrient intake or body mass loss.
  • Traditional malnutrition screening methods are often resource-intensive, time-consuming, and lack consistent accuracy and broad applicability.
  • Automated machine learning (ML) approaches present a potential solution for efficient and adaptable nutritional assessment.

Purpose of the Study:

  • To evaluate the efficacy of diverse machine learning models in predicting malnutrition.
  • To benchmark 22 different regression and classification models on a unified malnutrition dataset.
  • To optimize model efficiency by identifying minimal input feature requirements.

Main Methods:

  • A robust model development pipeline was implemented for reproducibility, utilizing a Neonatal Intensive Care Unit (NICU) patient dataset (412 patients, 232 for training).
  • A comprehensive range of ML models were tested, including linear models, tree-based models, neural networks, and ensemble methods.
  • Models were assessed for both regression and classification tasks to predict nutritional status.

Main Results:

  • The Generalized Linear Models with Lasso or Elastic Net Regularization (GLMnet) achieved the highest performance for the regression task, with an R-squared (R²) of 0.79.
  • The Extreme Gradient Boosting (XGBoost) model demonstrated superior performance for the classification task, yielding an Area Under the Curve (AUC) of 0.79.
  • Both GLMnet and XGBoost provided reliable automated nutritional assessment capabilities.

Conclusions:

  • Machine learning models provide effective and automated alternatives to traditional malnutrition screening tools.
  • The study successfully identified high-performing ML models (GLMnet for regression, XGBoost for classification) for nutritional assessment.
  • These automated methods can potentially reduce the burden on healthcare systems by offering efficient and accurate nutritional evaluations.