Comparison of statistical learning approaches for cerebral aneurysm rupture assessment.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jan 1, 2020

Abstract

PURPOSE: Incidental aneurysms pose a challenge to physicians who need to decide whether or not to treat them. A statistical model could potentially support such treatment decisions. The aim of this study was to compare a previously developed aneurysm rupture logistic regression probability model (LRM) to other machine learning (ML) classifiers for discrimination of aneurysm rupture status.

Authors

  • Felicitas J Detmer
    Bioengineering Department, Volgenau School of Engineering, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA. fdetmer@gmu.edu.
  • Daniel Lückehe
    Computational Health Informatics, Leibniz University Hanover, Schloßwender Str. 5, 30159, Hanover, Germany. lueckehe@chi.uni-hannover.de.
  • Fernando Mut
    Bioengineering Department, Volgenau School of Engineering, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA.
  • Martin Slawski
    Statistics Department, George Mason University, Fairfax, VA, USA.
  • Sven Hirsch
    Institute of Applied Simulation, ZHAW University of Applied Sciences, Wädenswil, Switzerland.
  • Philippe Bijlenga
    Neurosurgery, Clinical Neurosciences Department, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
  • Gabriele von Voigt
    Computational Health Informatics, Leibniz University Hanover, Schloßwender Str. 5, 30159, Hanover, Germany.
  • Juan R Cebral
    Bioengineering Department, Volgenau School of Engineering, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA.

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