BACKGROUND: @entity453 ( @entity453 ) is the result of the accumulation of @entity527 within the walls of coronary arteries, which supply the myocardium with @entity26 and nutrients. @entity453 leads to @entity299 or @entity64 and is, thus, one of the most important causes of @entity204 worldwide. Angiography, an imaging modality for blood vessels, is currently the most accurate method of diagnosing @entity2791 . However, the disadvantages of this method such as complications, costs, and possible side effects have prompted researchers to investigate alternative solutions. OBJECTIVES: 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 @entity1 who refer to hospitals due to @entity662 , a great number of them are normal and as such do not need angiography. The objective of this study was to predict @entity1 who are most probably normal using features with the highest correlations with @entity453 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. @entity1 AND METHODS: Different features were measured and collected from potential @entity1 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 @entity65 , 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 @entity453 @entity1 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 @entity1347 ( @entity1347 ), left circumflex (LCX), and right coronary artery (RCA), respectively. The accuracy to predict the @entity1347 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 @entity412 , hemoglobin, @entity101 , @entity2299 , @entity6 , and ejection fraction were extracted. CONCLUSIONS: The current study presents the highest accuracy value to diagnose the @entity1347 in the literature.

Diagnosing XXXX via Data Mining Algorithms by Considering Laboratory and Echocardiography Features.

@entity1 :: ('9606', 'Species') :: ['patients', 'people', 'PATIENTS'] @entity662 :: ('MESH:D002637', 'Disease') :: ['chest pain'] @entity64 :: ('MESH:D020521', 'Disease') :: ['strokes'] @entity527 :: ('MESH:D058225', 'Disease') :: ['athermanous plaques'] @entity101 :: ('MESH:D006973', 'Disease') :: ['hypertension'] @entity6 :: ('MESH:D003920', 'Disease') :: ['diabetes mellitus'] @entity2299 :: ('MESH:D050171', 'Disease') :: ['dyslipidemia'] @entity1347 :: ('MESH:C537775', 'Disease') :: ['left anterior descending', 'LAD', 'LAD stenosis'] @entity26 :: ('MESH:D010100', 'Chemical') :: ['oxygen'] @entity2791 :: ('MESH:D012078', 'Disease') :: ['artery stenosis'] @entity412 :: ('CHEBI:17855', 'Chemical') :: ['triglyceride'] @entity299 :: ('MESH:D006331', 'Disease') :: ['heart attacks'] @entity453 :: ('MESH:D003324', 'Disease') :: ['Coronary artery disease', 'CAD'] @entity204 :: ('MESH:D003643', 'Disease') :: ['death'] @entity65 :: ('MESH:D002318', 'Disease') :: ['Rajaie Cardiovascular']
@entity453 :: (MESH:D003324,Disease) :: ['Coronary Artery Disease']
