title: Diagnosing Coronary Artery Disease via Data Mining Algorithms by Considering Laboratory and Echocardiography Features.
abstract: BACKGROUND
  Coronary artery disease (CAD) is the result of the accumulation of athermanous plaques within the walls of coronary arteries, which supply the myocardium with oxygen and nutrients. CAD leads to heart attacks or strokes and is, thus, one of the most important causes of death worldwide. Angiography, an imaging modality for blood vessels, is currently the most accurate method of diagnosing artery stenosis. However, the disadvantages of this method such as complications, costs, and possible side effects have prompted researchers to investigate alternative solutions.
  
  
  OBJECTIVE
  The current study aimed to use data analysis, a non-invasive and less costly method, and various data mining algorithms to predict the stenosis of arteries. Among many people who refer to hospitals due to chest pain, a great number of them are normal and as such do not need angiography. The objective of this study was to predict patients who are most probably normal using features with the highest correlations with CAD with a view to obviate angiography costs and complications. Not a substitute for angiography, this method would select high-risk cases that definitely need angiography.
  
  
  METHODS
  Different features were measured and collected from potential patients in order to construct a dataset, which was later utilized for model extraction. Most of the proposed methods in the literature have not considered the stenosis of each artery separately, whereas the present study employed laboratory and echocardiographic data to diagnose the stenosis of each artery separately. The data were gathered from 303 random visitors to Rajaie Cardiovascular, Medical and Research Center. Electrocardiographic (ECG) data were studied in our previous works. The goal of this study was, therefore, to seek the accuracy of echocardiographic and laboratory features to predict CAD patients that require angiography.
  
  
  RESULTS
  Bagging and C4.5 classification algorithms were drawn upon to analyse the data, the former reaching accuracy rates of 79.54%, 61.46%, and 68.96% for the diagnosis of the stenoses of the left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA), respectively. The accuracy to predict the LAD stenosis was attained via feature selection. In the current study, features effective in the stenosis of arteries were further determined, and some rules for the evaluation of triglyceride, hemoglobin, hypertension, dyslipidemia, diabetes mellitus, and ejection fraction were extracted.
  
  
  CONCLUSIONS
  The current study presents the highest accuracy value to diagnose the LAD stenosis in the literature.
authors.affiliation: Department of Computer Engineering, Sharif University of Technology, Tehran, IR Iran.
authors.forename: Roohallah
authors.initials: R
authors.lastname: Alizadehsani
authors.affiliation: Department of Computer Engineering, Sharif University of Technology, Tehran, IR Iran.
authors.forename: Jafar
authors.initials: J
authors.lastname: Habibi
authors.affiliation: Rajaie Cardiovascular Medical and Research Center, Tehran University of Medical Science, Tehran, IR Iran.
authors.forename: Zahra
authors.initials: Z
authors.lastname: Alizadeh Sani
authors.affiliation: Department of Computer Engineering, Sharif University of Technology, Tehran, IR Iran.
authors.forename: Hoda
authors.initials: H
authors.lastname: Mashayekhi
authors.affiliation: Department of Computer Engineering, Sharif University of Technology, Tehran, IR Iran.
authors.forename: Reihane
authors.initials: R
authors.lastname: Boghrati
authors.affiliation: Department of Computer Engineering, Sharif University of Technology, Tehran, IR Iran.
authors.forename: Asma
authors.initials: A
authors.lastname: Ghandeharioun
authors.affiliation: Mashhad University of Medical Sciences, Mashhad, IR Iran.
authors.forename: Fahime
authors.initials: F
authors.lastname: Khozeimeh
authors.affiliation: Mashhad University of Medical Sciences, Mashhad, IR Iran.
authors.forename: Fariba
authors.initials: F
authors.lastname: Alizadeh-Sani
keywords: Coronary Artery Disease; Data Mining; Sensitivity and Specificity