Abstract
Objective and early detection of Alzheimer's disease (AD)
is a demanding problem requiring consideration of many
modal 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 |
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Title of host publication | Proceedings of the 32nd Annual International Conference of the IEEE EMBS 2010 |
Place of Publication | Piscataway |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 2886-2889 |
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 Sept 2010 |
Conference
Conference | 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 |
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Abbreviated title | EMBC'10 |
Country/Territory | Argentina |
City | Buenos Aires |
Period | 31/08/10 → 4/09/10 |
Keywords
- Alzheimer`s disease
- feature selection
- decision support
- classification
- data mining