Cerebral aneurysm rupture status classification using statistical and machine learning methods

@article{Amigo2021CerebralAR,
  title={Cerebral aneurysm rupture status classification using statistical and machine learning methods},
  author={Nicol{\'a}s Amigo and Alvaro Valencia and Wei Wu and Sourav S Patnaik and Ender A. Finol},
  journal={Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine},
  year={2021},
  volume={235},
  pages={655 - 662}
}
  • N. Amigo, A. Valencia, E. Finol
  • Published 8 March 2021
  • Computer Science
  • Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine
Morphological characterization and fluid dynamics simulations were carried out to classify the rupture status of 71 (36 unruptured, 35 ruptured) patient specific cerebral aneurysms using a machine learning approach together with statistical techniques. Eleven morphological and six hemodynamic parameters were evaluated individually and collectively for significance as rupture status predictors. The performance of each parameter was inspected using hypothesis testing, accuracy, confusion matrix… 

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References

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