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Scientific article
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Comparison of statistical learning approaches for cerebral aneurysm rupture assessment

Publication date2020
Abstract

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.

Keywords
  • Aneurysm, Ruptured/diagnosis/physiopathology
  • Decision Trees
  • Hemodynamics/physiology
  • Humans
  • Intracranial Aneurysm/diagnosis/physiopathology
  • Models, Statistical
  • ROC Curve
  • Support Vector Machine
Funding
  • Autre - @neurIST project funded by the EU commission (IST-2004-027703)
  • Autre - AneuX project evaluated by the Swiss National Science Foundation and funded by the SystemsX.ch initiative (MRD 2014/261)
Citation (ISO format)
DETMER, Felicitas J et al. Comparison of statistical learning approaches for cerebral aneurysm rupture assessment. In: International Journal of Computer Assisted Radiology and Surgery, 2020, vol. 15, n° 1, p. 141–150. doi: 10.1007/s11548-019-02065-2
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Article (Published version)
accessLevelRestricted
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Identifiers
ISSN of the journal1861-6410
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Technical informations

Creation10/10/2021 7:16:00 PM
First validation10/10/2021 7:16:00 PM
Update time03/16/2023 2:24:22 AM
Status update03/16/2023 2:24:20 AM
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