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Deep Neural Networks for Image-Based Dietary Assessment
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Advancing Nutritional Status Classification With Hybrid Artificial Intelligence: A Novel Methodological Approach.

Md Moddassir Alam1, Asif Irshad Khan2, Aasim Zafar2

  • 1Department of Health Information Management and Technology, College of Applied Medical Sciences, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia.

Brain and Behavior
|May 13, 2025
PubMed
Summary
This summary is machine-generated.

A new AI method using FHO-K-Means and EGBF accurately detects childhood malnutrition. This approach enhances early detection and intervention, aiming to reduce infant mortality and improve health outcomes in low-income countries.

Keywords:
child malnutritionchildrenclustering analysishybrid AI strategieshybrid modelmachine learning in nutritionmalnutrition detectionnutritional status analysis

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Area of Science:

  • Artificial Intelligence in Public Health
  • Machine Learning for Disease Classification
  • Nutritional Epidemiology

Background:

  • Malnutrition is a major global health challenge, particularly in low-income nations, causing over half of infant deaths.
  • Undernutrition compromises immune function, increasing vulnerability to infections and prolonging recovery.
  • Accurate and timely assessment of nutritional status is crucial for effective public health interventions.

Purpose of the Study:

  • To develop and validate a novel artificial intelligence (AI)-based classification method for assessing childhood nutritional status.
  • To enhance the accuracy and reliability of malnutrition detection using hybrid machine learning strategies.
  • To identify key physiological indicators for early identification of undernutrition in children.

Main Methods:

  • Utilized the fire hawk optimizer-based k-means (FHO-K-Means) clustering for identifying key indicators from the UNICEF dataset.
  • Applied extreme gradient boosting fuzzy (EGBF) classification on partitioned training and testing sets.
  • Classified nutritional states including stunting, wasting, severe wasting, overweight, and underweight.

Main Results:

  • The FHO-K-Means and EGBF model achieved high performance: 99.84% accuracy, 99.5% precision, 99.8% specificity, and 100% sensitivity.
  • The model demonstrated a 98.6% F1 score and a minimal mean squared error (MSE) of 0.01%.
  • Outperformed existing classification techniques, offering a scalable tool for identifying at-risk populations.

Conclusions:

  • Developed an innovative FHO-K-Means clustering and EGBF classification method for childhood malnutrition assessment.
  • The model's exceptional accuracy and predictive power support early detection and data-driven public health policy.
  • This approach can significantly reduce childhood morbidity and improve health outcomes in resource-constrained settings.