Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

The use of novel analytical techniques (such as data clustering and decision trees) that can model and predict patient disease outcomes has great potential for assessing disease process and progression in Alzheimer's disease and mild cognitive impairment. For this study, 124 different variables (generated from image data, demographics and data) have been compiled and analyzed using a modified clustering algorithm. Our aim was to determine the influence of these variables on the incidence of Alzheimer's and mild cognitive impairment. Furthermore, we used a decision tree algorithm to model the level of "importance" of variants influencing this decision. © 2011 by IJAI.

Type

Journal article

Journal

International Journal of Artificial Intelligence

Publication Date

01/03/2011

Volume

6

Pages

90 - 99