Supporting Real World Decision Making in Coronary Diseases Using Machine Learning
| العنوان: | Supporting Real World Decision Making in Coronary Diseases Using Machine Learning |
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| المؤلفون: | Tajda Bogovič, Tadej Završnik, Helena Blažun Vošner, Jan Jurman, Peter Kokol, Jernej Završnik |
| المصدر: | Inquiry: The Journal of Health Care Organization, Provision, and Financing, Vol 58 (2021) Inquiry: A Journal of Medical Care Organization, Provision and Financing |
| بيانات النشر: | SAGE Publishing, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Computer science, Decision Making, knowledge discovery, Coronary Disease, heuristics, 030204 cardiovascular system & hematology, Coronary disease, Machine learning, computer.software_genre, cross-validation, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Knowledge extraction, Humans, 030212 general & internal medicine, Original Research, business.industry, Health Policy, cardiovascular conditions, artificial intelligence, Artificial intelligence, Neural Networks, Computer, Public aspects of medicine, RA1-1270, Heuristics, business, diagnosing, computer, Algorithms |
| الوصف: | 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. |
| اللغة: | English |
| تدمد: | 1945-7243 0046-9580 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd21060d934ad9b4976eb610fcf74550 https://doaj.org/article/c119550e9585444c812b3e6eac51085b |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....dd21060d934ad9b4976eb610fcf74550 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19457243 00469580 |
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