A speedy cardiovascular diseases classifier using multiple criteria decision analysis

Wah Ching Lee, Faan Hei Hung, Kim Fung Tsang, Hoi Ching Tung, Wing Hong Lau, Veselin Rakocevic, Loi Lei Lai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1312-1320
Number of pages9
JournalSensors (Switzerland)
Issue number1
Publication statusPublished - 12 Jan 2015


  • Analytic hierarchy process
  • Cardiovascular diseases classifier
  • Electrocardiogram
  • Multiple criteria decision analysis
  • Support vector machine

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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