Publication Type

Conference Proceeding Article

Publication Date

8-2015

Abstract

In multi-view learning, multimodal representations of a real world object or situation are integrated to learn its overall picture. Feature sets from distinct data sources carry different, yet complementary, information which, if analysed together, usually yield better insights and more accurate results. Neuro-degenerative disorders such as dementia are characterized by changes in multiple biomarkers. This work combines the features from neuroimaging and cerebrospinal fluid studies to distinguish Alzheimer's disease patients from healthy subjects. We apply statistical data fusion techniques on 101 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We examine whether fusion of biomarkers helps to improve diagnostic accuracy and how the methods compare against each other for this problem. Our results indicate that multimodal data fusion improves classification accuracy.

Keywords

Alzheimer's disease, Data fusion, Heterogeneous, Multimodal

Discipline

Databases and Information Systems | Health Information Technology

Research Areas

Data Management and Analytics

Publication

MEDINFO 2015: eHealth-enabled Health

Volume

216

First Page

731

Last Page

735

ISBN

9781614995630

Identifier

10.3233/978-1-61499-564-7-731

Publisher

IOS Press

City or Country

The Netherlands

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.3233/978-1-61499-564-7-731

Share

COinS