Machine learning-based stratification of prediabetes and type 2 diabetes progression.

Journal: Diabetology & metabolic syndrome
Published Date: Jun 18, 2025

Abstract

BACKGROUND: Diabetes mellitus, a global health concern with severe complications, demands early detection and precise staging for effective management. Machine learning approaches, combined with bioinformatics, offer promising avenues for enhancing diagnostic accuracy and identifying key biomarkers.

Authors

  • Marwa Matboli
    Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
  • Abdelrahman Khaled
    Bioinformatics Group, Center of Informatics Sciences (CIS), School of Information Technology and Computer Sciences, Nile University, Giza, Egypt.
  • Manar Fouad Ahmed
    Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Ain Shams University, Cairo, 11566, Egypt.
  • Manar Yehia Ahmed
    Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Ain Shams University, Cairo, 11566, Egypt.
  • Radwa Khaled
    Biotechnology/Biomolecular Chemistry Department, Faculty of Science, Cairo University, Cairo, Egypt.
  • Gena M Elmakromy
    Endocrinology & Diabetes Mellitus Unit, Department of Internal Medicine, Badr University in Cairo, Badr City, Egypt.
  • Amani Mohamed Abdel Ghani
    Clinical Pathology, Faculty of Medicine, Ain Shams University, Cairo 11566, Egypt. Electronic address: dr_amani83@med.asu.edu.eg.
  • Marwa M El-Shafei
    Pathology Department, Faculty of Oral and Dental Medicine, Misr International University, Cairo, Egypt.
  • Marwa Ramadan M Abdelhalim
    Clinical Pathology, Faculty of Medicine, Ain Shams University, Cairo, 11566, Egypt.
  • Asmaa Mohamed Abd El Gwad
    Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Ain Shams University, Cairo, 11566, Egypt.

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