An ensemble classification approach for automated diagnosis of Alzheimer's disease and mild cognitive impairment

Effective and robust detection of Alzheimer disease (AD) and mild cognitive impairment (MCI), which is a prodromal stage of AD, has attracted a lot of attention in the recent years. So far, multiple feature types such as hippocampal volume and shape, voxel based morphometry, and cortical thickness based features extracted from structural magnetic resonance imaging (MRI) have been proved to be effective for the diagnosis of MCI and AD. This work specifically deals with the features calculated from cortical surface. In particular, the techniques based on regional features of cortical surface poorly reveal the spatial variation in the cortical thickness, and the features defined on vertices are noise sensitive due to the large number of dimensions in MRI data. In this study, we present an ensemble classification framework developed on the average and vertex-wise features of cortical thickness. Furthermore, we also combine thickness based features with volume based features of cortex. The features were reduced using F-Score feature selection method prior to classification. The proposed technique has been tested on a dataset of 60 AD, 60 MCI, and 60 normal controls (NC) from Alzheimer Disease Neuro imaging Initiative (ADNI) dataset, and improvement in performance has been observed compared to previously reported studies in terms of sensitivity, specificity, accuracy and kappa statistics. The results show that the proposed feature selection coupled with ensemble classification significantly improves performance.