CV


Hamid Saadatfar

Hamid Saadatfar

Associate Professor

Faculty: Electrical and Computer Engineering

Department: Computer

Degree: Ph.D

CV
Hamid Saadatfar

Associate Professor Hamid Saadatfar

Faculty: Electrical and Computer Engineering - Department: Computer Degree: Ph.D |

Dr. Hamid Saadatfar is currently an assistant professor of Computer Engineering Department at University of Birjand. He has received his B.Sc., M.Sc., and Ph.D. degrees from Ferdowsi university of Mashhad in 2007, 2009 and 2014, respectively. His research interests include:

  • Parallel and Distributed Processing (Cluster, Grid and Cloud Computing),
  • Data Mining and Machine Learning,
  • Big Data Analysis (Data Mining Methods for Big Data)
  • and Power-aware Computing.

نمایش بیشتر

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

AuthorsHamid Saadatfar,Edris Hassannataj joloudari,Mohammad GhasemiGol,Amir Mosavi,Narjes Nabipour,Shahaboddin Shamshirband,Laszlo Nadai
JournalInternational Journal of Environmental Research and Public Health
Page number1-24
Paper TypeFull Paper
Published At2020
Journal GradeISI
Journal TypeTypographic
Journal CountrySwitzerland
Journal IndexISI،JCR،Scopus

Abstract

Abstract: Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, the coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that RTs model outperforms other models.

Paper URL