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Feature selection using factor analysis for Alzheimer's diagnosis using F 18 ‐ FDG PET images
Author(s) -
SalasGonzalez D.,
Górriz J. M.,
Ramírez J.,
Illán I. A.,
López M.,
Segovia F.,
Chaves R.,
Padilla P.,
Puntonet C. G.
Publication year - 2010
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3488894
Subject(s) - artificial intelligence , voxel , pattern recognition (psychology) , support vector machine , feature selection , kernel (algebra) , linear discriminant analysis , dimensionality reduction , computer science , multivariate statistics , gaussian function , gaussian , mathematics , machine learning , combinatorics , physics , quantum mechanics
Purpose: This article presents a computer‐aided diagnosis technique for improving the accuracy of the early diagnosis of Alzheimer's disease (AD). Two hundred and ten F18 ‐ FDG PET images from the ADNI initiative [52 normal controls (NC), 114 mild cognitive impairment (MCI), and 53 AD subjects] are studied. Methods: The proposed methodology is based on the selection of voxels of interest using the t‐test and a posterior reduction of the feature dimension using factor analysis. Factor loadings are used as features for three different classifiers: Two multivariate Gaussian mixture model, with linear and quadratic discriminant function, and a support vector machine with linear kernel. Results: An accuracy rate up to 95% when NC and AD are considered and an accuracy rate up to 88% and 86% for NC‐MCI and NC‐MCI, AD, respectively, are obtained using SVM with linear kernel. Conclusions: Results are compared to the voxel‐as‐features and a PCA‐ based approach and the proposed methodology achieves better classification performance.