An integrated machine learning framework for cerebral aneurysm rupture status classification using clinical, morphological, and hemodynamic features.

Zakeri M, Atef A, Aziznia M, Jafari A

DOI: 10.1371/journal.pone.0360923
Published: 2026
Container: PloS one
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pone.0360923,
  title = {An integrated machine learning framework for cerebral aneurysm rupture status classification using clinical, morphological, and hemodynamic features.},
  author = {Zakeri M and Atef A and Aziznia M and Jafari A},
  year = {2026},
  journal = {PloS one},
  doi = {10.1371/journal.pone.0360923},
  url = {https://doi.org/10.1371/journal.pone.0360923}
}

RIS

TY  - JOUR
TI  - An integrated machine learning framework for cerebral aneurysm rupture status classification using clinical, morphological, and hemodynamic features.
AU  - Zakeri M
AU  - Atef A
AU  - Aziznia M
AU  - Jafari A
PY  - 2026
JO  - PloS one
DO  - 10.1371/journal.pone.0360923
UR  - https://doi.org/10.1371/journal.pone.0360923
ER  -

APA

M, Z., A, A., M, A., & A, J. (2026). An integrated machine learning framework for cerebral aneurysm rupture status classification using clinical, morphological, and hemodynamic features.. PloS one. https://doi.org/10.1371/journal.pone.0360923

Source records

pubmed · retrieved 2026-11-10T10:13:27.683Z