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Comparison of classification methods for tissue outcome after ischaemic stroke
Author(s) -
Tozlu Ceren,
Ozenne Brice,
Cho TaeHee,
Nighoghossian Norbert,
Mikkelsen Irene Klærke,
Derex Laurent,
Hermier Marc,
Pedraza Salvador,
Fiehler Jens,
Østergaard Leif,
Berthezène Yves,
Baron JeanClaude,
MaucortBoulch Delphine
Publication year - 2019
Publication title -
european journal of neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.346
H-Index - 206
eISSN - 1460-9568
pISSN - 0953-816X
DOI - 10.1111/ejn.14507
Subject(s) - random forest , receiver operating characteristic , artificial intelligence , support vector machine , logistic regression , machine learning , artificial neural network , magnetic resonance imaging , gradient boosting , triage , computer science , medicine , pattern recognition (psychology) , radiology , emergency medicine
In acute ischaemic stroke, identifying brain tissue at high risk of infarction is important for clinical decision‐making. This tissue may be identified with suitable classification methods from magnetic resonance imaging data. The aim of the present study was to assess and compare the performance of five popular classification methods (adaptive boosting, logistic regression, artificial neural networks, random forest and support vector machine) in identifying tissue at high risk of infarction on human voxel‐based brain imaging data. The classification methods were used with eight MRI parameters, including diffusion‐weighted imaging and perfusion‐weighted imaging obtained in 55 patients. The five criteria used to assess the performance of the methods were the area under the receiver operating curve (AUC roc ), the area under the precision–recall curve (AUC pr ), sensitivity, specificity and the Dice coefficient. The methods performed equally in terms of sensitivity and specificity, while the results of AUC roc and the Dice coefficient were significantly better for adaptive boosting, logistic regression, artificial neural networks and random forest. However, there was no statistically significant difference between the performances of these five classification methods regarding AUC pr , which was the main comparison metric. Machine learning methods can provide valuable prognostic information using multimodal imaging data in acute ischaemic stroke, which in turn can assist in developing personalized treatment decision for clinicians after a thorough validation of methods with an independent data set.