Accurate Prediction of Coronary Artery Disease Using Bioinformatics Algorithms.
Background and Objectives: Cardiovascular disease is one of the main causes of death in developed and Third World countries. According to the statement of the World Health Organization, it is predicted that death due to heart disease will rise to 23 million by 2030. According to the latest statistic...
| Publicado en: | Qom University of Medical Sciences Journal Vol. 10; no. 4; pp. 1 - 4 |
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| Autores principales: | , |
| Formato: | abstract research Journal Article |
| Publicado: |
Qom University of Medical Sciences
Jun2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=118708203&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118708203 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17357799 8P2V jtl: Qom University of Medical Sciences Journal issn: 17357799 maglogo: N pubinfo: dt: Jun2016 vid: 10 iid: 4 pid: 50887 pub: Qom University of Medical Sciences artinfo: ui: 118708203 118708203 118708203 118708203 ppf: 1 ppct: 3 formats: fmt: @attributes: type: P tig: atl: Accurate Prediction of Coronary Artery Disease Using Bioinformatics Algorithms. aug: au: Shafiee, Hajar Ebrahimi, Mansour sug: subj: Coronary Arteriosclerosis Bioinformatics Algorithms Decision Trees Cluster Analysis Data Mining ab: Background and Objectives: Cardiovascular disease is one of the main causes of death in developed and Third World countries. According to the statement of the World Health Organization, it is predicted that death due to heart disease will rise to 23 million by 2030. According to the latest statistics reported by Iran's Minister of health, 3.39% of all deaths are attributed to cardiovascular diseases and 19.5% are related to myocardial infarction. The aim of this study was to predict coronary artery disease using data mining algorithms. Methods: In this study, various bioinformatics algorithms, such as decision trees, neural networks, support vector machines, clustering, etc., were used to predict coronary heart disease. The data used in this study was taken from several valid databases (including 14 data). Results: In this research, data mining techniques can be effectively used to diagnose different diseases, including coronary artery disease. Also, for the first time, a prediction system based on support vector machine with the best possible accuracy was introduced. Conclusion: The results showed that among the features, thallium scan variable is the most important feature in the diagnosis of heart disease. Designation of machine prediction models, such as support vector machine learning algorithm can differentiate between sick and healthy individuals with 100% accuracy. pubtype: Academic Journal doctype: abstract research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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