By Topic

A data mining approach for dyslipidemia disease prediction using carotid arterial feature vectors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Minghao Piao ; Database/Bioinf. Lab., Chungbuk Nat. Univ., Cheongju, South Korea ; Heon Gyu Lee ; Couchol Pok ; Keun Ho Ryu

In this paper, we proposed a useful methodology for the diagnosis of dyslipidemia disease by using novel various features of carotid arterial wall thickness. We measured and tested intima-media thickness of carotid arteries and used them as diagnostic feature vectors. In order to evaluate extracted various features, we tested on five classification methods and evaluated performance of classifiers. As a result, SVM and Neural Network algorithms (about 92%-98% goodness of fit) outperformed the other classifiers on those selected features.

Published in:

Computer Engineering and Technology (ICCET), 2010 2nd International Conference on  (Volume:2 )

Date of Conference:

16-18 April 2010