Abstract：
BACKGROUND:Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia in the elderly population. Scientific research is very active in the challenge of designing automated approaches to achieve an early and certain diagnosis. Recently an international competition among AD predictors has been organized: "A Machine learning neuroimaging challenge for automated diagnosis of Mild Cognitive Impairment" (MLNeCh). This competition is based on pre-processed sets of T1-weighted Magnetic Resonance Images (MRI) to be classified in four categories: stable AD, individuals with MCI who converted to AD, individuals with MCI who did not convert to AD and healthy controls. NEW METHOD:In this work, we propose a method to perform early diagnosis of AD, which is evaluated on MLNeCh dataset. Since the automatic classification of AD is based on the use of feature vectors of high dimensionality, different techniques of feature selection/reduction are compared in order to avoid the curse-of-dimensionality problem, then the classification method is obtained as the combination of Support Vector Machines trained using different clusters of data extracted from the whole training set. RESULTS:The multi-classifier approach proposed in this work outperforms all the stand-alone method tested in our experiments. The final ensemble is based on a set of classifiers, each trained on a different cluster of the training data. The proposed ensemble has the great advantage of performing well using a very reduced version of the data (the reduction factor is more than 90%). The MATLAB code for the ensemble of classifiers will be publicly available1 to other researchers for future comparisons.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Nanni L,Lumini A,Zaffonato Ndoi
10.1016/j.jneumeth.2017.11.002subject
Has Abstractpub_date
2018-05-15 00:00:00pages
42-46eissn
0165-0270issn
1872-678Xpii
S0165-0270(17)30382-5journal_volume
302pub_type
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journal_title：Journal of neuroscience methods
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journal_title：Journal of neuroscience methods
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