{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T13:44:24Z","timestamp":1762004664841,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2015,1,12]],"date-time":"2015-01-12T00:00:00Z","timestamp":1421020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s150101312","type":"journal-article","created":{"date-parts":[[2015,1,12]],"date-time":"2015-01-12T13:30:46Z","timestamp":1421069446000},"page":"1312-1320","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Speedy Cardiovascular Diseases Classifier Using Multiple Criteria Decision Analysis"],"prefix":"10.3390","volume":"15","author":[{"given":"Wah","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong, China"}]},{"given":"Faan","family":"Hung","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China"}]},{"given":"Kim","family":"Tsang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China"}]},{"given":"Hoi","family":"Tung","sequence":"additional","affiliation":[{"name":"School of Engineering & Mathematical Science, City University London, Northampton Square, London EC1V 0HB, UK"}]},{"given":"Wing","family":"Lau","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China"}]},{"given":"Veselin","family":"Rakocevic","sequence":"additional","affiliation":[{"name":"School of Engineering & Mathematical Science, City University London, Northampton Square, London EC1V 0HB, UK"}]},{"given":"Loi","family":"Lai","sequence":"additional","affiliation":[{"name":"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"}]}],"member":"1968","published-online":{"date-parts":[[2015,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"573","DOI":"10.3390\/s120100573","article-title":"Visual Sensor Based Abnormal Event Detection with Moving Shadow Removal in Home Healthcare Applications","volume":"12","author":"Lee","year":"2012","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12623","DOI":"10.3390\/s140712623","article-title":"Implementation of a Data Packet Generator Using Pattern Matching for Wearable ECG Monitoring Systems","volume":"14","author":"Noh","year":"2014","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5994","DOI":"10.3390\/s140405994","article-title":"A Novel Approach to ECG Classification Based upon Two-layered HMMs in Body Sensor Networks","volume":"14","author":"Liang","year":"2014","journal-title":"Sensors"},{"key":"ref_4","first-page":"1","article-title":"Prediction of Cardiovascular Risk Using Framingham, ASSIGN and QRISK2: How Well Do They Predict Individual Rather than Population Risk?","volume":"9","author":"Staa","year":"2014","journal-title":"PLoS One"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.asoc.2013.11.009","article-title":"Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system","volume":"20","author":"Sanz","year":"2014","journal-title":"Appl. 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