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European Radiology
Published in: European Radiology 5/2019

01-05-2019 | Letter to the Editor

Use of artificial neural networks to predict anterior communicating artery aneurysm rupture: possible methodological considerations

Authors: Guido Adriaan de Jong, René Aquarius

Published in: European Radiology | Issue 5/2019

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Key Points

  • Use of algorithms to generate synthetic cases might result in a misrepresentation of the entire population.
  • Training an artificial neural network with a mix of real and synthetic data might lead to non-realistic prediction precision.
Literature
1.
2.
He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. IEEE International Joint Conference on Neural Networks, pp 1322–1328
3.
Tang B, He H (2015) KernelADASYN: kernel based adaptive synthetic data generation for imbalanced learning. IEEE Congress on Evolutionary Computation, pp 664–671
4.
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958
5.
Boughorbel S, Jarray F, El-Anbari M (2017) Optimal classifier for imbalanced data using Matthews correlation coefficient metric. PLoS One 12:1–17CrossRef
Metadata
Title
Use of artificial neural networks to predict anterior communicating artery aneurysm rupture: possible methodological considerations
Authors
Guido Adriaan de Jong
René Aquarius
Publication date
01-05-2019
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 5/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-018-5794-3

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