Survival analysis for lung cancer patients: A comparison of Cox regression and machine learning models.

Journal: International journal of medical informatics
PMID: 39208536

Abstract

INTRODUCTION: Survival analysis based on cancer registry data is of paramount importance for monitoring the effectiveness of health care. As new methods arise, the compendium of statistical tools applicable to cancer registry data grows. In recent years, machine learning approaches for survival analysis were developed. The aim of this study is to compare the model performance of the well established Cox regression and novel machine learning approaches on a previously unused dataset.

Authors

  • Sebastian Germer
    German Research Center for Artificial Intelligence (DFKI), Ratzeburger Allee 160, 23562 Lübeck, Germany. Electronic address: sebastian.germer@dfki.de.
  • Christiane Rudolph
    Institute for Cancer Epidemiology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany.
  • Louisa Labohm
    Institute for Social Medicine and Epidemiology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany.
  • Alexander Katalinic
    Institute for Cancer Epidemiology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; Institute for Social Medicine and Epidemiology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany.
  • Natalie Rath
    Saarland Cancer Registry, Neugeländstraße 9, 66117 Saarbrücken, Germany.
  • Katharina Rausch
    Saarland Cancer Registry, Neugeländstraße 9, 66117 Saarbrücken, Germany.
  • Bernd Holleczek
    Saarland Cancer Registry, Neugeländstraße 9, 66117 Saarbrücken, Germany.
  • Heinz Handels
    Institute of Medical Informatics, University of Lübeck, Lübeck, Germany.

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