Associated factors of white matter hyperintensity volume: a machine-learning approach

Associated factors of white matter hyperintensity volume: a machine-learning approach

Abstract To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). WMH volumes were derived from cMRI-images...

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Détails bibliographiques
Auteurs principaux: Sergio Grosu, Susanne Rospleszcz, Felix Hartmann, Mohamad Habes, Fabian Bamberg, Christopher L. Schlett, Franziska Galie, Roberto Lorbeer, Sigrid Auweter, Sonja Selder, Robin Buelow, Margit Heier, Wolfgang Rathmann, Katharina Mueller-Peltzer, Karl-Heinz Ladwig, Hans J. Grabe, Annette Peters, Birgit B. Ertl-Wagner, Sophia Stoecklein
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
Medicine
R
Science
Q
Accès en ligne:https://doaj.org/article/3e861650a14c4153b0cc0080e7208f3d
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Internet

https://doaj.org/article/3e861650a14c4153b0cc0080e7208f3d

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