Publication Type
Conference Proceeding Article
Version
publishedVersion
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
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
Citation
Pillai, P. S. and Tze-Yun LEONG.
Fusing Heterogeneous Data for Alzheimer's Disease Classification. (2015). MEDINFO 2015: eHealth-enabled Health. 216, 731-735.
Available at: https://ink.library.smu.edu.sg/sis_research/3019
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.3233/978-1-61499-564-7-731