Physiological model-based machine learning for classifying patients with binge-eating disorder (BED) from the Oral Glucose Tolerance Test (OGTT) curve.

Journal: Computer methods and programs in biomedicine
PMID: 39509761

Abstract

BACKGROUND AND OBJECTIVE: Binge eating disorder (BED) is the most frequent eating disorder, often confused with obesity, with which it shares several characteristics. Early identification could enable targeted therapeutic interventions. In this study, we propose a hybrid pipeline that, starting from plasma glucose data acquired during the Oral Glucose Tolerance Test (OGTT), allows us to classify the two types of patients through computational modeling and artificial intelligence.

Authors

  • Anna Procopio
    Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italia.
  • Marianna Rania
    Outpatient Unit for Clinical Research and Treatment of Eating Disorders, University Hospital Mater Domini, Catanzaro, Italy.
  • Paolo Zaffino
    Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, 88100, Catanzaro, Italy.
  • Nicola Cortese
    Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, Italy.
  • Federica Giofrè
    Internal Medicine Unit, Department of Medical and Surgical Sciences, Università degli Studi Magna Græcia, Catanzaro, Italy.
  • Franco Arturi
    Internal Medicine Unit, Department of Medical and Surgical Sciences, Università degli Studi Magna Græcia, Catanzaro, Italy.
  • Cristina Segura-Garcia
    Outpatient Unit for Clinical Research and Treatment of Eating Disorders, University Hospital Mater Domini, Catanzaro, Italy; Department of Medical and Surgical Sciences, Università degli Studi Magna Græcia, Catanzaro, Italy.
  • Carlo Cosentino

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