Supporting Real World Decision Making in Coronary Diseases Using Machine Learning.
Cardiovascular diseases are one of the leading global causes of death. Following the positive experiences with machine learning in medicine we performed a study in which we assessed how machine learning can support decision making regarding coronary artery diseases. While a plethora of studies repor...
| Publicado en: | Inquiry (00469580) pp. 1 - 7 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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Sage Publications Inc.
5/17/2021
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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=hlh&AN=150365467&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 150365467 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 5/17/2021 pid: 344 pub: Sage Publications Inc. artinfo: ui: 150365467 10.1177/0046958021997338 ppf: 1 ppct: 6 formats: tig: atl: Supporting Real World Decision Making in Coronary Diseases Using Machine Learning. aug: au: Kokol, Peter Jurman, Jan Bogovič, Tajda Završnik, Tadej Završnik, Jernej Blažun Vošner, Helena affil: Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia University Clinical Medical Centre Maribor, Maribor, Slovenia Community Heathcare Center dr. Adolf Drolc Maribor, Maribor, Slovenia Alma Mater Europaea-ECM, Maribor, Slovenia Faculty of Natural Sciences and Mathematics University of Maribor, Maribor, Slovenia Faculty of Health and Social Sciences Slovenj Gradec, Slovenj Gradec, Slovenia su: Cardiovascular disease diagnosis Machine learning Artificial intelligence Health literacy Phenomenology Decision making Hospital information systems Artificial neural networks Algorithms sug: subj: Cardiovascular disease diagnosis Machine learning Artificial intelligence Health literacy Phenomenology Decision making Hospital information systems Artificial neural networks Algorithms keyword: artificial intelligence cardiovascular conditions cross-validation decision making diagnosing heuristics knowledge discovery machine learning ab: Cardiovascular diseases are one of the leading global causes of death. Following the positive experiences with machine learning in medicine we performed a study in which we assessed how machine learning can support decision making regarding coronary artery diseases. While a plethora of studies reported high accuracy rates of machine learning algorithms (MLA) in medical applications, the majority of the studies used the cleansed medical data bases without the presence of the "real world noise." Contrary, the aim of our study was to perform machine learning on the routinely collected Anonymous Cardiovascular Database (ACD), extracted directly from a hospital information system of the University Medical Centre Maribor). Many studies used tens of different machine learning approaches with substantially varying results regarding accuracy (ACU), hence they were not usable as a base to validate the results of our study. Thus, we decided, that our study will be performed in the 2 phases. During the first phase we trained the different MLAs on a comparable University of California Irvine UCI Heart Disease Dataset. The aim of this phase was first to define the "standard" ACU values and second to reduce the set of all MLAs to the most appropriate candidates to be used on the ACD, during the second phase. Seven MLAs were selected and the standard ACUs for the 2-class diagnosis were 0.85. Surprisingly, the same MLAs achieved the ACUs around 0.96 on the ACD. A general comparison of both databases revealed that different machine learning algorithms performance differ significantly. The accuracy on the ACD reached the highest levels using decision trees and neural networks while Liner regression and AdaBoost performed best in UCI database. This might indicate that decision trees based algorithms and neural networks are better in coping with real world not "noise free" clinical data and could successfully support decision making concerned with coronary diseasesmachine learning. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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