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Learning Classification Trees for Personalized Cardiovascular Risk Stratification

Learning Classification Trees for Personalized Cardiovascular Risk Stratification

This video was recorded at NIPS Workshops, Sierra Nevada 2011. 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.

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