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

EasyChair Preprint 2467

Abstract

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), the decision tree of C5.0, support vector machine (SVM), the 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.

Keyphrases: Health Informatics, Heart Disease, Predictive Features, coronary artery disease, coronary artery disease diagnosis, machine learning, prediction

@booklet{EasyChair:2467,
  author    = {Javad Hassannataj Joloudari and Edris Hassannataj Joloudari and Hamid Saadatfar and Mohammad Ghasemigol and Seyyed Mohammad Razavi and Amir Mosavi and Narjes Nabipour and Shahab Shamshirband and Laszlo Nadai},
  title     = {Coronary Artery Disease Diagnosis; Ranking the Significant Features Using Random Trees Model},
  howpublished = {EasyChair Preprint 2467},
  year      = {EasyChair, 2020}}