Publications by authors named "Hamid Saadatfar"

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

Int J Environ Res Public Health 2020 01 23;17(3). Epub 2020 Jan 23.

Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary.

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, 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), and 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 the RTs model outperforms other models.
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http://dx.doi.org/10.3390/ijerph17030731DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7037941PMC
January 2020