Comparison of Random Forest and Artificial Neural Network Models to Evaluate Diagnostic Factors in the Necessity to Perform Angiography
| Author | Parastoo Golpour | en |
| Author | Mohammad Tajfard | en |
| Author | Majid Ghayour-Mobarhan | en |
| Author | Mohsen Moohebati | en |
| Author | Ali Taghipour | en |
| Author | habibollah Esmaily | en |
| Author | Sara Sabbaghian Tousi | en |
| Orcid | Parastoo Golpour [0000-0001-7147-9539] | en |
| Orcid | Mohammad Tajfard [0000-0002-2867-8759] | en |
| Orcid | Majid Ghayour-Mobarhan [0000-0002-1081-6754] | en |
| Orcid | Ali Taghipour [0000-0001-7594-0097] | en |
| Orcid | habibollah Esmaily [0000-0003-4139-546X] | en |
| Orcid | Sara Sabbaghian Tousi [0000-0003-4234-6344] | en |
| Issued Date | 2022-06-30 | en |
| Abstract | Background: Coronary Artery Disease (CAD) is the most common type of cardiovascular disorders. Despite being costly and invasive, coronary angiography is a reliable method for diagnosing CAD. Therefore, it is crucial to use non-invasive methods to screen candidates for angiography to accelerate the process of decision-making. Two powerful Machine Learning (ML) methods are Random Forest (RF) and Artificial Neural Network (ANN). | en |
| DOI | https://doi.org/ | en |
| Keyword | Artificial Neural Network | en |
| Keyword | Random Forest | en |
| Keyword | Angiography | en |
| Keyword | Machine Learning | en |
| Keyword | Coronary Artery Disease | en |
| Keyword | Risk factor | en |
| Publisher | Shiraz University of Medical Sciences | en |
| Title | Comparison of Random Forest and Artificial Neural Network Models to Evaluate Diagnostic Factors in the Necessity to Perform Angiography | en |
| Type | Research Article | en |