Untitled Document
Title: Random forest feature selection, fusion and ensemble strategy
No: 11375 Writer: 최용호 :4 2018-07-12
Background: In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer’s dis- ease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI. New method: Based on preprocessed MRI images from the organizers of a neuroimaging challenge,3 we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multi- ple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting.