Advancing Nutritional Status Classification With Hybrid Artificial Intelligence: A Novel Methodological Approach.

Journal: Brain and behavior
PMID: 40356452

Abstract

PURPOSE: Malnutrition remains a critical public health issue in low-income countries, significantly hindering economic development and contributing to over 50% of infant deaths. Under nutrition weakens immune systems, increasing susceptibility to common illnesses and prolonging recovery periods. This study aims to develop and evaluate a novel artificial intelligence-based classification method for nutritional status assessment using hybrid machine learning strategies, enhancing the accuracy and reliability of malnutrition detection.

Authors

  • Md Moddassir Alam
    Department of Health Information Management and Technology, College of Applied Medical Sciences, University of Hafr Al Batin, Hafr Al Batin, 39524, Saudi Arabia.
  • Asif Irshad Khan
    Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
  • Aasim Zafar
    Department of Computer Science, Aligarh Muslim University, Aligarh, India.
  • Mohammad Sohail
    Data Architect and AI/ML Engineer, Hewlett Packard Enterprise, Pennsylvania, USA.
  • Mohammad Tauheed Ahmad
    College of Medicine, King Khalid University, Abha, Saudi Arabia. Electronic address: moahmad@kku.edu.sa.
  • Rezaul Azim
    Faculty of Science, University of Chittagong, Chittagong, Bangladesh.

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