Using Machine Learning Algorithms in Cardiovascular Disease Risk Evaluation
Even if Medicine and Computer Science seemapparently intangible domains, they collaborate each otherfor few decades. One of the faces of this cooperation is DataMining, a relative new and multidisciplinary field capable toextract valuable information from large sets of data. Despitethis fact, in car...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Stefan cel Mare University of Suceava
2009-01-01
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| Series: | Journal of Applied Computer Science & Mathematics |
| Subjects: | |
| Online Access: | http://www.jacs.usv.ro/getpdf.php?issue=5&paperid=54 |
| _version_ | 1818446469598806016 |
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| author | D. A. Sitar-Taut D. Pop D. Zdrenghea A. V. Sitar-Taut |
| author_facet | D. A. Sitar-Taut D. Pop D. Zdrenghea A. V. Sitar-Taut |
| author_sort | D. A. Sitar-Taut |
| collection | DOAJ |
| description | Even if Medicine and Computer Science seemapparently intangible domains, they collaborate each otherfor few decades. One of the faces of this cooperation is DataMining, a relative new and multidisciplinary field capable toextract valuable information from large sets of data. Despitethis fact, in cardiology related studies it was rarely used. Weassume that some data mining tools can be used as asubstitute for some complex, expensive, uncomfortable, timeconsuming, and sometimes dangerous medical examinations.This paper aims to show that cardiovascular diseases may bepredicted by classical risk factors analyzed and processed ina “non-invasive” way. |
| first_indexed | 2024-12-14T19:48:13Z |
| format | Article |
| id | doaj.art-275abbd3182347f49998c192545454e6 |
| institution | Directory Open Access Journal |
| issn | 2066-4273 2066-3129 |
| language | English |
| last_indexed | 2024-12-14T19:48:13Z |
| publishDate | 2009-01-01 |
| publisher | Stefan cel Mare University of Suceava |
| record_format | Article |
| series | Journal of Applied Computer Science & Mathematics |
| spelling | doaj.art-275abbd3182347f49998c192545454e62022-12-21T22:49:29ZengStefan cel Mare University of SuceavaJournal of Applied Computer Science & Mathematics2066-42732066-31292009-01-01352932Using Machine Learning Algorithms in Cardiovascular Disease Risk EvaluationD. A. Sitar-TautD. PopD. ZdrengheaA. V. Sitar-TautEven if Medicine and Computer Science seemapparently intangible domains, they collaborate each otherfor few decades. One of the faces of this cooperation is DataMining, a relative new and multidisciplinary field capable toextract valuable information from large sets of data. Despitethis fact, in cardiology related studies it was rarely used. Weassume that some data mining tools can be used as asubstitute for some complex, expensive, uncomfortable, timeconsuming, and sometimes dangerous medical examinations.This paper aims to show that cardiovascular diseases may bepredicted by classical risk factors analyzed and processed ina “non-invasive” way.http://www.jacs.usv.ro/getpdf.php?issue=5&paperid=54cardiovascular diseasemachine learning algorithms |
| spellingShingle | D. A. Sitar-Taut D. Pop D. Zdrenghea A. V. Sitar-Taut Using Machine Learning Algorithms in Cardiovascular Disease Risk Evaluation Journal of Applied Computer Science & Mathematics cardiovascular disease machine learning algorithms |
| title | Using Machine Learning Algorithms in Cardiovascular Disease Risk Evaluation |
| title_full | Using Machine Learning Algorithms in Cardiovascular Disease Risk Evaluation |
| title_fullStr | Using Machine Learning Algorithms in Cardiovascular Disease Risk Evaluation |
| title_full_unstemmed | Using Machine Learning Algorithms in Cardiovascular Disease Risk Evaluation |
| title_short | Using Machine Learning Algorithms in Cardiovascular Disease Risk Evaluation |
| title_sort | using machine learning algorithms in cardiovascular disease risk evaluation |
| topic | cardiovascular disease machine learning algorithms |
| url | http://www.jacs.usv.ro/getpdf.php?issue=5&paperid=54 |
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