Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk.

Journal: BMC medical research methodology
Published Date: Dec 29, 2018

Abstract

BACKGROUND: The use of Cardiovascular Disease (CVD) risk estimation scores in primary prevention has long been established. However, their performance still remains a matter of concern. The aim of this study was to explore the potential of using ML methodologies on CVD prediction, especially compared to established risk tool, the HellenicSCORE.

Authors

  • Alexandros C Dimopoulos
    2Department of Informatics & Telematics,School of Digital Technology,Harokopio University,17676 Athens,Greece.
  • Mara Nikolaidou
    Department of Informatics & Telematics, School of Digital Technology, Harokopio University, Athens, Greece.
  • Francisco Félix Caballero
    Department of Psychiatry, Universidad Autónoma de Madrid, Madrid, Spain.
  • Worrawat Engchuan
    The Centre for Applied Genomics, Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.
  • Albert Sánchez-Niubó
    Parc Sanitari Sant Joan de Déu, Barcelona, Spain.
  • Holger Arndt
    SPRING TECHNO GMBH &Co. KG, Bremen, Germany.
  • José Luis Ayuso-Mateos
    Department of Psychiatry, Universidad Autónoma de Madrid, Madrid, Spain.
  • Josep Maria Haro
    CIBER Salud Mental (CIBERSAM), Madrid, Spain.
  • Somnath Chatterji
    Information, Evidence and Research, World Health Organization, Geneva, Switzerland.
  • Ekavi N Georgousopoulou
    Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece.
  • Christos Pitsavos
    4School of Medicine,University of Athens,11527 Athens,Greece.
  • Demosthenes B Panagiotakos
    Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece.

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