Using machine learning techniques to differentiate acute coronary syndrome
| Author | Sougand Setareh | en |
| Author | Ali Asghar Safaei | en |
| Author | Farid Najafi | en |
| Issued Date | 2015-02-28 | en |
| Abstract | Backgroud: Acute coronary syndrome (ACS) is an unstable and dynamic process that includes unstable angina, ST elevation myocardial infarction, and non-ST elevation myocardial infarction. Despite recent technological advances in early diognosis of ACS, differentiating between different types of coronary diseases in the early hours of admission is controversial. The present study was aimed to accurately differentiate between various coronary events, using machine learning techniques. Such methods, as a subset of artificial intelligence, include algorithms that allow computers to learn and play a major role in treatment decisions. | en |
| DOI | https://doi.org/10.22110/jkums.v18i11.2023 | en |
| Keyword | Acute Coronary Syndrome | en |
| Keyword | diagnosis | en |
| Keyword | machine learning | en |
| Keyword | decision tree | en |
| Keyword | bagging | en |
| Keyword | bagging. | en |
| Publisher | Brieflands | en |
| Title | Using machine learning techniques to differentiate acute coronary syndrome | en |
| Type | Letter | en |
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