Identification and prediction of association patterns between nutrient intake and anemia using machine learning techniques: results from a cross-sectional study with university female students from Palestine.

Journal: European journal of nutrition
PMID: 38512358

Abstract

PURPOSE: This study utilized data mining and machine learning (ML) techniques to identify new patterns and classifications of the associations between nutrient intake and anemia among university students.

Authors

  • Radwan Qasrawi
    Department of Computer Science, Al-Quds University, Jerusalem, Palestine.
  • Manal Badrasawi
    Department of Nutrition and Food Technology, Faculty of Agriculture and Veterinary Medicine, An-Najah National University, Nablus, West Bank, Palestine.
  • Diala Abu Al-Halawa
    Department of Computer Science, Al-Quds University, Jerusalem, Palestine.
  • Stephanny Vicuna Polo
    Department of Computer Science, Al-Quds University, Jerusalem, Palestine.
  • Rami Abu Khader
    Department of Computer Science, Al-Quds University, Jerusalem, Palestine.
  • Haneen Al-Taweel
    Department of Computer Science, Al-Quds University, Jerusalem, Palestine.
  • Reem Abu Alwafa
    Department of Nutrition and Food Technology, Faculty of Agriculture and Veterinary Medicine, An-Najah National University, Nablus, West Bank, Palestine.
  • Rana Zahdeh
    Department of Applied Chemistry and Biology, College of Applied Sciences, Palestine Polytechnic University, Hebron, West Bank, Palestine.
  • Andreas Hahn
    Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; and.
  • Jan Philipp Schuchardt
    Institute of Food Science and Human Nutrition, Leibniz University Hannover, Hannover, Germany. schuchardt@nutrition.uni-hannover.de.

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