Random forest feature selection, fusion and ensemble strategy: combining multiple morphological MRI measures to discriminate among healthy elderly, MCI, cMCI and Alzheimer's disease patients: from the Alzheimer's disease neuroimaging initiative (ADNI) database

Dimitriadis, Stavros ORCID: https://orcid.org/0000-0002-0000-5392, Liparas, D and Tsolaki, Magda N 2018. Random forest feature selection, fusion and ensemble strategy: combining multiple morphological MRI measures to discriminate among healthy elderly, MCI, cMCI and Alzheimer's disease patients: from the Alzheimer's disease neuroimaging initiative (ADNI) database. Journal of Neuroscience Methods 302 , pp. 14-23. 10.1016/j.jneumeth.2017.12.010

Preview

PDF - Accepted Post-Print Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (910kB) | Preview

Abstract

Background In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer’s disease (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 multiple 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. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 CE were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme. Results In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition. Comparison with existing method(s) The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature. Conclusions Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG) Cardiff University Brain Research Imaging Centre (CUBRIC) Medicine Psychology
Publisher:	Elsevier
ISSN:	0165-0270
Date of First Compliant Deposit:	18 December 2017
Date of Acceptance:	17 December 2017
Last Modified:	28 Nov 2024 13:00
URI:	https://orca.cardiff.ac.uk/id/eprint/107633

Citation Data

Cited 68 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item