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Sensors 2015, 15(1), 1312-1320; doi:10.3390/s150101312

A Speedy Cardiovascular Diseases Classifier Using Multiple Criteria Decision Analysis

1
Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong, China
2
Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
3
School of Engineering & Mathematical Science, City University London, Northampton Square, London EC1V 0HB, UK
4
Energy Strategy, Planning, Policy Support, R&D Centre, State Grid Energy Research Institute, SGCC Administrative Area, Future Science and Technology Park, Changping, Beijing 102209, China
*
Author to whom correspondence should be addressed.
Received: 11 November 2014 / Accepted: 5 January 2015 / Published: 12 January 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [824 KB, uploaded 12 January 2015]   |  

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. View Full-Text
Keywords: cardiovascular diseases classifier; electrocardiogram; multiple criteria decision analysis; analytic hierarchy process; support vector machine cardiovascular diseases classifier; electrocardiogram; multiple criteria decision analysis; analytic hierarchy process; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lee, W.C.; Hung, F.H.; Tsang, K.F.; Tung, H.C.; Lau, W.H.; Rakocevic, V.; Lai, L.L. A Speedy Cardiovascular Diseases Classifier Using Multiple Criteria Decision Analysis. Sensors 2015, 15, 1312-1320.

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