Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease).

Journal: Journal of biomedical informatics
Published Date: Sep 1, 2019

Abstract

AIM: The aim of this study is to compare the utility of several supervised machine learning (ML) algorithms for predicting clinical events in terms of their internal validity and accuracy. The results, which were obtained using two statistical software platforms, were also compared.

Authors

  • Juan-Jose Beunza
    Machine Learning Health Working Group, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain; Department of Medicine, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain. Electronic address: juanjo@juanjobeunza.com.
  • Enrique Puertas
    Machine Learning Health Working Group, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain; Department of Computer Science and Technology, School of Architecture, Engineering and Design, Universidad Europea de Madrid, Madrid, Spain.
  • Ester García-Ovejero
    Machine Learning Health Working Group, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain; Department of Nursing and Psychology, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain.
  • Gema Villalba
    Machine Learning Health Working Group, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain; Indra, Madrid, Spain.
  • Emilia Condes
    Machine Learning Health Working Group, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain.
  • Gergana Koleva
    Machine Learning Health Working Group, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain.
  • Cristian Hurtado
    Machine Learning Health Working Group, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain; Department of Pharmacy and Biotechnology, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain.
  • Manuel F Landecho
    Machine Learning Health Working Group, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain; Departament of Internal Medicine, Clinica Universidad de Navarra, Pamplona, Spain.

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