{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T10:23:55Z","timestamp":1771237435306,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2016,6,13]],"date-time":"2016-06-13T00:00:00Z","timestamp":1465776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT &amp; Future Planning","award":["(No.2013R1A2A2A01068923)."],"award-info":[{"award-number":["(No.2013R1A2A2A01068923)."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In this paper, we proposed not only an extraction methodology of multiple feature vectors from ultrasound images for carotid arteries (CAs) and heart rate variability (HRV) of electrocardiogram signal, but also a suitable and reliable prediction model useful in the diagnosis of cardiovascular disease (CVD). For inventing the multiple feature vectors, we extract a candidate feature vector through image processing and measurement of the thickness of carotid intima-media (IMT). As a complementary way, the linear and\/or nonlinear feature vectors are also extracted from HRV, a main index for cardiac disorder. The significance of the multiple feature vectors is tested with several machine learning methods, namely Neural Networks, Support Vector Machine (SVM), Classification based on Multiple Association Rule (CMAR), Decision tree induction and Bayesian classifier. As a result, multiple feature vectors extracted from both CAs and HRV (CA+HRV) showed higher accuracy than the separative feature vectors of CAs and HRV. Furthermore, the SVM and CMAR showed about 89.51% and 89.46%, respectively, in terms of diagnosing accuracy rate after evaluating the diagnosis or prediction methods using the finally chosen multiple feature vectors. Therefore, the multiple feature vectors devised in this paper can be effective diagnostic indicators of CVD. In addition, the feature vector analysis and prediction techniques are expected to be helpful tools in the decisions of cardiologists.<\/jats:p>","DOI":"10.3390\/sym8060047","type":"journal-article","created":{"date-parts":[[2016,6,13]],"date-time":"2016-06-13T10:29:22Z","timestamp":1465813762000},"page":"47","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["A Data Mining Approach for Cardiovascular Disease Diagnosis Using Heart Rate Variability and Images of Carotid Arteries"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4523-2572","authenticated-orcid":false,"given":"Hyeongsoo","family":"Kim","sequence":"first","affiliation":[{"name":"College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5315-7314","authenticated-orcid":false,"given":"Musa","family":"Ishag","sequence":"additional","affiliation":[{"name":"College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea"}]},{"given":"Minghao","family":"Piao","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Dongguk University Kyeongju Campus, Gyeongju 38066, Korea"}]},{"given":"Taeil","family":"Kwon","sequence":"additional","affiliation":[{"name":"Bigsun Systems Co., Ltd., Seoul 06266, Korea"}]},{"given":"Keun","family":"Ryu","sequence":"additional","affiliation":[{"name":"College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2016,6,13]]},"reference":[{"key":"ref_1","unstructured":"WHO Available online: http:\/\/www.who.int\/mediacentre\/factsheets\/fs310\/en\/."},{"key":"ref_2","unstructured":"Korea National Statistical Office Available online: http:\/\/kosis.kr\/statHtml\/statHtml.do?orgId=101&tblId=DT_1B34E02&conn_path=I2."},{"key":"ref_3","first-page":"208","article-title":"Comparison of clinical outcomes between culprit vessel only and multivessel percutaneous coronary intervention for ST-segment elevation myocardial infarction patients with multivessel coronary diseases","volume":"12","author":"Ryu","year":"2015","journal-title":"J. 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