Coronary Artery Disease Diagnosis; Ranking the Significant Features Using Random Trees Model

Since data collection and analysis are difficult, time consuming and costly, we are always looking for a way to optimum use of data to achieve the correct decision that can be referred to diagnose and experiment of diseases in healthcare organizations [3]. In addition, common method such as angiography [5,6] in experimenting and diagnosing diseases is costly and have adverse effects for patients as healthcare resear chers are trying to utilize methods that avoid the high cost as well as the adverse effects of previous methods, which can be performed by using computer - aided disease diagnose methods means machine learning. Whereas, da ta mining process by utilizing machine learning science and database management knowledge [1] has become a robust tool for data analysis and management of health industry data which ultimately leads to knowledge extraction. It should be noted that, with the progress of technology in t he healthcare especially, healthcare industry 4.0, human lifetime has become progressive and more comfortable [ 7 ] . In this new generation, with the development of new medical devices, equipment and tools, new knowledge can be gained in the field of disease diagnosis.