Cart (Loading....) | Create Account
Close category search window
 

Chronic Hepatitis Classification Using SNP Data and Data Mining Techniques

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

5 Author(s)
Saangyong Uhmn ; Dept. of Comput. Eng., Hallym Univ., Chuncheon ; Dong-Hoi Kim ; Jin Kim ; Sung Won Cho
more authors

The machine learning techniques, SVM, decision tree, and decision rule, are used to predict the susceptibility to the liver disease, chronic hepatitis from single nucleotide polymorphism(SNP) data. Also, they are used to identify a set of SNPs relevant to the disease. In addition, we apply backtracking technique to couple of feature selection algorithms, forward selection and backward elimination, and show that this technique is beneficial to find the better solutions by experiments. The experimental results show that decision rule is able to distinguish chronic hepatitis from normal with the maximum accuracy of 73.20%, whereas SVM is with 67.53% and decision tree is with 72.68%. It is also shown that decision tree and decision rule are potential tools to predict the susceptibility to chronic hepatitis from SNP data.

Published in:

Frontiers in the Convergence of Bioscience and Information Technologies, 2007. FBIT 2007

Date of Conference:

11-13 Oct. 2007

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.