Learning Classification Trees for Personalized Cardiovascular Risk Stratification
published: Jan. 23, 2012, recorded: December 2011, views: 4251
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Cardiovascular disease is the leading cause of death worldwide. There are many effective treatments available, but identifying high-risk patients who are most likely to benefit from various therapies is an unsolved problem. Risk stratification would benefit from the development of principled data-driven methods to systematically combine prognostic information from many risk variables into a clinically useful classification tree. In this paper, we present a classification tree induction algorithm, and show that it produces trees that can be used for personalized cardiovascular risk stratification. A challenge in doing this is the high class imbalance in medical datasets. Our algorithm uses non-symmetric entropy measures for two critical tasks in classification tree learning: discretization of continuous variables and assigning a variable to a node. We tested our algorithm on 4219 cardiovascular patients for two different risk stratification tasks: prediction of cardiovascular death and myocardial infarction. For both tasks, our classification tree-based models outperformed other types of classification trees and SVMs.
Download slides: nipsworkshops2011_singh_stratification_01.pdf (217.6 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !