Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression.

Journal: Psychiatry research
PMID: 33667949

Abstract

BACKGROUND: Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive performances of traditional logistic regression, basic probabilistic machine learning (ML) methods, and automated ML (Auto-sklearn).

Authors

  • Wessel A van Eeden
    Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: W.A.van_Eeden@lumc.nl.
  • Chuan Luo
    Leiden Institute of Advanced Computer Sciences, Leiden University, Leiden, the Netherlands.
  • Albert M van Hemert
    Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands.
  • Ingrid V E Carlier
    Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands.
  • Brenda W Penninx
    Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, VU University Medical Center, and GGZ inGeest, Amsterdam, the Netherlands.
  • Klaas J Wardenaar
    Department of Psychiatry, The University Medical Center Groningen, Groningen, the Netherlands.
  • Holger Hoos
    Leiden Institute of Advanced Computer Sciences, Leiden University, Leiden, the Netherlands.
  • Erik J Giltay
    Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands.

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