title: A speedy cardiovascular diseases classifier using multiple criteria decision analysis.
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.
authors.affiliation: Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong, China. enwclee@polyu.edu.hk.
authors.forename: Wah Ching
authors.initials: WC
authors.lastname: Lee
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China. fhhung4@cityu.edu.hk.
authors.forename: Faan Hei
authors.initials: FH
authors.lastname: Hung
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China. ee330015@cityu.edu.hk.
authors.forename: Kim Fung
authors.initials: KF
authors.lastname: Tsang
authors.affiliation: School of Engineering & Mathematical Science, City University London, Northampton Square, London EC1V 0HB, UK. cherry.tung.1@city.ac.uk.
authors.forename: Hoi Ching
authors.initials: HC
authors.lastname: Tung
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China. itwhlau@cityu.edu.hk.
authors.forename: Wing Hong
authors.initials: WH
authors.lastname: Lau
authors.affiliation: School of Engineering & Mathematical Science, City University London, Northampton Square, London EC1V 0HB, UK. V.Rakocevic@city.ac.uk.
authors.forename: Veselin
authors.initials: V
authors.lastname: Rakocevic
authors.affiliation: 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. lailaili@sgeri.sgcc.com.cn.
authors.forename: Loi Lei
authors.initials: LL
authors.lastname: Lai
mesh_terms: D000465:Algorithms; D002318:Cardiovascular Diseases / Q000145:classification*; D019992:Databases as Topic; D003661:Decision Support Techniques; D004562:Electrocardiography; D006801:Humans