Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease.

08:00 EDT 3rd April 2019 | BioPortfolio

Summary of "Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease."

In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at the individual level. In this study, we aimed to classify each individual subject into one of the diagnostic categories in a hierarchical manner by employing a machine learning-based classification method.


Journal Details

This article was published in the following journal.

Name: NeuroImage. Clinical
ISSN: 2213-1582
Pages: 101811


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