Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Analysis of a Dobutamine Stress Echocardiography Dataset Using Rough Sets
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
The rough set exploration system
Transactions on Rough Sets III
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Stress echocardiography is an important functional diagnostic and prognostic tool that is now routinely applied to evaluate the risk of cardiovascular artery disease (CAD). In patients who are unable to safely undergo a stress based test, dobutamine is administered which provides a similar effect to stress on the cardiovascular system. In this work, a complete dataset containing data on 558 subjects undergoing a prospective longitudinal study is employed to investigate what diagnostic features correlate with the final outcome. The dataset was examined using rough sets, which produced a series of decision rules that predicts which features influence the outcomes measured clinically and recorded in the dataset. The results indicate that the ECG attribute was the most informative diagnostic feature. In addition, prehistory information has a significant impact on the classification accuracy.