Classification of α-thalassemia data using machine learning models.

Journal: Computer methods and programs in biomedicine
PMID: 39798280

Abstract

BACKGROUND: Around 7% of the global population has congenital hemoglobin disorders, with over 300,000 new cases of α-thalassemia annually. Diagnosis is costly and inaccurate in low-income regions, often relying on complete blood count (CBC) tests. This study employs machine learning (ML) to classify α-thalassemia traits based on gender and CBC, exploring the effects of grouping silent- and non-carriers.

Authors

  • Frederik Christensen
    Operations Research Group, Department of Materials and Production, Aalborg University, Aalborg, 9220, Denmark. Electronic address: frch@mp.aau.dk.
  • Deniz Kenan Kılıç
    Operations Research Group, Department of Materials and Production, Aalborg University, Aalborg, 9220, Denmark.
  • Izabela Ewa Nielsen
    Operations Research Group, Department of Materials and Production, Aalborg University, Aalborg, 9220, Denmark.
  • Tarec Christoffer El-Galaly
    Department of Haematology, Aalborg University Hospital, Aalborg, 9000, Denmark.
  • Andreas Glenthøj
    Danish Red Blood Cell Center, Department of Hematology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, 2100, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, 2100, Denmark.
  • Jens Helby
    Danish Red Blood Cell Center, Department of Hematology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, 2100, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, 2100, Denmark.
  • Henrik Frederiksen
    Jorne L. Biccler, Lasse Hjort Jakobsen, Martin Bøgsted, and Tarec C. El-Galaly, Aalborg University Hospital and Aalborg University, Aalborg; Peter de Nully Brown, Copenhagen University Hospital, Copenhagen; Henrik Frederiksen, Odense University Hospital, Odense; Judit Jørgensen, Aarhus University Hospital, Aarhus; Denmark; Sandra Eloranta and Karin E. Smedby, Karolinska Institutet, Stockholm; Mats Jerkeman, Lund University, Lund; and Karin E. Smedby, Karolinska University Hospital, Solna, Sweden.
  • Soren Möller
    Open Patient Data Explorative Network, OPEN, Odense University Hospital and Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
  • Alexander Djupnes Fuglkjær
    Operations Research Group, Department of Materials and Production, Aalborg University, Aalborg, 9220, Denmark.

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