Expanding interpretability through complexity reduction in machine learning-based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study.

Journal: European journal of clinical investigation
PMID: 40191939

Abstract

BACKGROUND: Machine learning-based analysis can be used in myocardial perfusion imaging data to improve risk stratification and the prediction of major adverse cardiovascular events for patients with suspected or established coronary artery disease. We present a new machine learning approach for the identification of patients who develop major adverse cardiovascular events. The new method is robust against the deleterious effect of outliers in the training set stratification and training process.

Authors

  • Eero Lehtonen
    Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland.
  • Jarmo Teuho
    Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland.
  • Monire Vatandoust
    Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland.
  • Juhani Knuuti
    Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland.
  • Remco J J Knol
    Cardiac Imaging Division Alkmaar, Department of Nuclear Medicine, Northwest Clinics, Alkmaar, The Netherlands.
  • Friso M van der Zant
    Cardiac Imaging Division Alkmaar, Department of Nuclear Medicine, Northwest Clinics, Alkmaar, The Netherlands.
  • Luis Eduardo Juarez-Orozco
    Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland. l.e.juarez.orozco@gmail.com.
  • Riku Klén
    Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.

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