An ECG-based machine-learning approach for mortality risk assessment in a large European population.

Journal: Journal of electrocardiology
Published Date:

Abstract

AIMS: Through a simple machine learning approach, we aimed to assess the risk of all-cause mortality after 5 years in a European population, based on electrocardiogram (ECG) parameters, age, and sex.

Authors

  • Martina Doneda
    Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
  • Ettore Lanzarone
    Department of Management, Information and Production Engineering, University of Bergamo, Dalmine (BG), Italy.
  • Claudio Giberti
    University of Modena and Reggio Emilia, Department of Sciences and Methods for Engineering, Via G. Amendola 2, 42122 Reggio Emilia, Italy.
  • Cecilia Vernia
    University of Modena and Reggio Emilia, Department of Physics, Informatics and Mathematics, Via Campi 213/b, 41125 Modena, Italy.
  • Andi Vjerdha
    University of Modena and Reggio Emilia, Department of Physics, Informatics and Mathematics, Via Campi 213/b, 41125 Modena, Italy.
  • Federico Silipo
    Health Authority and Services and Azienda Ospedaliero-Universitaria of Modena, Department of Clinical Engineering, Via del Pozzo 71, 41100 Modena, Italy.
  • Paolo Giovanardi
    Health Authority and Services of Modena, Department of Primary Care, Via del Pozzo 71, 41100 Modena, Italy; Modena University Hospital, S. Agostino-Estense Hospital, Via Giardini 1355, 41126 Baggiovara, Modena, Italy. Electronic address: p.giovanardi@ausl.mo.it.