Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches.

Journal: Frontiers in endocrinology
Published Date: Nov 11, 2024

Abstract

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the integration of machine learning (ML) and explainable artificial intelligence (XAI) approaches based on metabolomics panel data to identify biomarkers and develop predictive models for T2DM.

Authors

  • Ahmet Kadir Arslan
    Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Türkiye.
  • Fatma Hilal Yagin
    Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Türkiye.
  • Abdulmohsen Algarni
    Computer Science, King Khalid University, Abha, Saudi Arabia.
  • Erol Karaaslan
    Department of Anesthesiology and Reanimation, Faculty of Medicine, Inonu University, Malatya, Türkiye.
  • Fahaid Al-Hashem
    Department of Physiology, College of Medicine, King Khalid University, Abha, Saudi Arabia.
  • Luca Paolo Ardigò
    Department of Teacher Education, NLA University College, Oslo, Norway.

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