Association rule-based feature selection method for Alzheimer’s disease diagnosis

A fundamental challenge that remains unsolved in the neuroimage field is the small sample size problem. Feature selection and extraction, which are based on a limited training set, are likely to display poor generalization performance on new datasets. To address this challenge, a novel voxel selection method based on association rule (AR) mining is proposed for designing a computer aided diagnosis (CAD) system. The proposed method is tested as a tool for the early diagnosis of Alzheimer’s disease (AD). Discriminant brain areas are selected from a single photon emission computed tomography (SPECT) or positron emission tomography (PET) databases by means of an AR mining process. Simultaneously activated brain regions in control subjects that consist of the set of voxels defining the antecedents and consequents of the ARs are selected as input voxels for posterior dimensionality reduction. Feature extraction is defined by a subsequent reduction of the selected voxels using principal component analysis (PCA) or partial least squares (PLS) techniques while classification is performed by a support vector machine (SVM). The proposed method yields an accuracy up to 91.75% (with 89.29% sensitivity and 95.12% specificity) for SPECT and 90% (with 89.33% sensitivity and 90.67% specificity) for PET, thus improving recently developed methods for early diagnosis of AD.

► Computer aided diagnosis (CAD) system design for the early diagnosis of Alzheimer’s disease (AD).
► Voxel selection by means of association rule (AR)-based method and feature extraction with PCA or PLS.
► Tested on a 97-SPECT image database and 150-PET image ADNI database.
► Image classification is performed by kernel support vector machines (SVM).
► Accuracy rates obtained are over 90% for SPECT and PET images.