Premium
Predictive Models Based on Support Vector Machines: Whole‐Brain versus Regional Analysis of Structural MRI in the Alzheimer's Disease
Author(s) -
Retico Alessandra,
Bosco Paolo,
Cerello Piergiorgio,
Fiorina Elisa,
Chincarini Andrea,
Fantacci Maria Evelina
Publication year - 2014
Publication title -
journal of neuroimaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.822
H-Index - 64
eISSN - 1552-6569
pISSN - 1051-2284
DOI - 10.1111/jon.12163
Subject(s) - support vector machine , voxel , artificial intelligence , medicine , pattern recognition (psychology) , neuroimaging , receiver operating characteristic , magnetic resonance imaging , feature (linguistics) , grey matter , cognitive impairment , alzheimer's disease neuroimaging initiative , disease , machine learning , computer science , pathology , white matter , radiology , linguistics , philosophy , psychiatry
Decision‐making systems trained on structural magnetic resonance imaging data of subjects affected by the Alzheimer's disease (AD) and healthy controls (CTRL) are becoming widespread prognostic tools for subjects with mild cognitive impairment (MCI). This study compares the performances of three classification methods based on support vector machines (SVMs), using as initial sets of brain voxels (ie, features): (1) the segmented grey matter (GM); (2) regions of interest (ROIs) by voxel‐wise t ‐test filtering; (3) parceled ROIs, according to prior knowledge. The recursive feature elimination (RFE) is applied in all cases to investigate whether feature reduction improves the classification accuracy. We analyzed more than 600 AD Neuroimaging Initiative (ADNI) subjects, training the SVMs on the AD/CTRL dataset, and evaluating them on a trial MCI dataset. The classification performance, evaluated as the area under the receiver operating characteristic curve (AUC), reaches AUC = (88.9 ± .5)% in 20‐fold cross‐validation on the AD/CTRL dataset, when the GM is classified as a whole. The highest discrimination accuracy between MCI converters and nonconverters is achieved when the SVM‐RFE is applied to the whole GM: with AUC reaching (70.7 ± .9)%, it outperforms both ROI‐based approaches in predicting the AD conversion.