By Topic

Information Visualization for Chronic Disease Risk Assessment

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

3 Author(s)

Here, the authors describe and evaluate a new information-visualization method and prototype software tool that support risk assessment for negative health outcomes. Their framework uses principal component analysis and linear discriminant analysis to plot high-dimensional patient data in 2D. It also incorporates interactive visualization techniques to aid the identification of high versus low risk patients, critical risk factors, and the estimated effect of hypothetical interventions on the likelihood of negative outcomes. The authors quantitatively evaluated the visualization method using a secondary dataset describing 588 people with diabetes and their estimated future risk of heart attack. Their results show that the method visually classifies high- and low-risk people with accuracy that's similar to other common statistical methods. The framework also provides an interactive, visualization-based tool for clinicians to explore the nuances of their patients' data and disease risk.

Published in:

Intelligent Systems, IEEE  (Volume:27 ,  Issue: 6 )