Prediction of Outcome in the Vegetative State by Machine Learning Algorithms: A Model for Clinicians?
Loris Pignolo, Vincenzo i Lagan
DOI: 10.4236/jsea.2011.46044   PDF    HTML     4,854 Downloads   8,801 Views   Citations


Purpose of this study was to compare different Machine Learning classifiers (C4.5, Support Vector Machine, Naive Bayes, K-NN) in the early prediction of outcome of the subjects in vegetative state due to traumatic brain injury. Accuracy proved acceptable for all compared methods (AUC > 0.8), but sensitivity and specificity varied considerably and only some classifiers (in particular, Support Vector Machine) appear applicable models in the clinical routine. A combined use of classifiers is advisable.

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Pignolo, L. and Lagan, V. (2011) Prediction of Outcome in the Vegetative State by Machine Learning Algorithms: A Model for Clinicians?. Journal of Software Engineering and Applications, 4, 388-390. doi: 10.4236/jsea.2011.46044.

Conflicts of Interest

The authors declare no conflicts of interest.


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