DIAGNOSING CARDIOVASCULAR DISEASE USING AN ENHANCED ROUGH SETS APPROACH

Authors:
Ching-Hsue Cheng;Jr-Shian Chen
Affiliations:
Department of Information Management, National Yunlin University of Science and Technology, Touliu, Yunlin, Taiwan;Department of Information Management, National Yunlin University of Science and Technology, Touliu, Yunlin, Taiwan,Department of Computer Science and Information Management, Hungkuang, University, ...
Venue:
Applied Artificial Intelligence
Year:
2009

Citing 5
Cited 0

A new version of the rule induction system LERS

Fundamenta Informaticae
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
Rough set algorithms in classification problem

Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Extracting classification rule of software diagnosis using modified MEPA

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Cardiovascular disease is a chronic disease and an ongoing threat to human health. Clinical data, including chemistry analysis data and electrocardiogram (ECG) data for heartbeat behavior, are commonly used to classify the cardiovascular diseases in supporting medical diagnosis. This study proposes a new approach for enhancing rough set classifier which applied to diagnose cardiovascular disease. Two datasets were used in this empirical case study to illustrate the proposed approach. Due to its improved accuracy and fewer rules, the proposed approach is superior to listing methods.