Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9932
Title: A speedy cardiovascular diseases classifier using multiple criteria decision analysis
Authors: Lee, WC 
Hung, FH
Tsang, KF
Tung, HC
Lau, WH
Rakocevic, V
Lai, LL
Keywords: Analytic hierarchy process
Cardiovascular diseases classifier
Electrocardiogram
Multiple criteria decision analysis
Support vector machine
Issue Date: 2015
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Sensors, 2015, v. 15, no. 1, p. 1312-1320 How to cite?
Journal: Sensors 
Abstract: Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA) to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented.
URI: http://hdl.handle.net/10397/9932
EISSN: 1424-8220
DOI: 10.3390/s150101312
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