NDLI: Application of different classification techniques on brain morphological data

Content Provider	IEEE Xplore Digital Library
Author	Sarica, A. Critelli, C. Guzzi, P.H. Cerasa, A. Quattrone, A. Cannataro, M.
Copyright Year	2013
Description	Author affiliation: Dept. of Surg. & Med. Sci., Magna Graecia Univ. of Catanzaro, Catanzaro, Italy (Sarica, A.; Critelli, C.; Guzzi, P.H.; Quattrone, A.; Cannataro, M.) \|\| Neuroimaging Res. Unit, Catanzaro, Italy (Cerasa, A.)
Abstract	The increasing number of people affected by Neurodegenerative diseases and the improvement of brain imaging diagnostic techniques are bringing to a massive production of brain images that need demanding preprocessing and analysis algorithms. We analyzed volumetric measures of critical brain areas by using different Data Mining methods. Structural magnetic resonance images, generated in our university, were preprocessed using a fully automated segmentation method and the extracted volumetric information was then analyzed by using different binary classifiers. We performed three binary classification experiments considering different data mining algorithms and neurological diseases. Naïve Bayes outperformed all the others classifiers in two experiments, obtaining respectively 93.75% and 95.00% accuracy, while in the third experiment the best classifier was SVM but with a lower accuracy (58,56%). Afterwards, using the Stacking technique we combined the predictions from the best detected three models to build a meta-learner. Meta-learner classification results suggest that the application of the Stacking technique needs more experimentation and the test of additional stackers.
Starting Page	425
Ending Page	428
File Size	398230
Page Count	4
File Format	PDF
ISBN	9781479910533
DOI	10.1109/CBMS.2013.6627832
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-20
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Data mining Support vector machines Stacking Diseases Classification algorithms Magnetic resonance imaging MRI data mining classification meta-learning stacking neuroscience neurogenerative disease
Content Type	Text
Resource Type	Article

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