Abstract
Objective and early detection of Alzheimer's
disease (AD) is a demanding problem requiring
consideration
of manymodal
observations. Potentially, many features could
be used to discern between people without AD and those at
different stages of the disease. Such features include
results
from cognitive and memory tests, imaging (MRI, PET)
results,
cerebral spine fluid data, blood markers etc. However, in
order to define an efficient and limited set of features
that can
be employed in classifiers requires mining of data from
many
patient cases. In this study we used two databases, ADNI
and
Kuopio LMCI,
to investigate the relative importance of
features and their combinations. Optimal feature
combinations are to be used in a Clinical Decision
Support
System that is to be used in clinical AD diagnosis
practice.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology |
| Publisher | IEEE Institute of Electrical and Electronic Engineers |
| ISBN (Print) | 978-1-4244-4124-2, 978-1-4244-4123-5 |
| DOIs | |
| Publication status | Published - 2010 |
| MoE publication type | A4 Article in a conference publication |
| Event | 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina Duration: 31 Aug 2010 → 4 Sep 2010 |
Conference
| Conference | 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 |
|---|---|
| Abbreviated title | EMBC'10 |
| Country/Territory | Argentina |
| City | Buenos Aires |
| Period | 31/08/10 → 4/09/10 |
Keywords
- personal health systems
- wellness
- behavioral change
- patient compliance