diagnosis of coronary arteries stenosis using data mining

Cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. coronary artery disease(cad) is the most common type of these diseases worldwide and is the main reason of heart attacks. thus early diagnosis of cad is veryessential and is an important field of medical studies. many methods are used to diagnose cad so far. these methods reduce cost anddeaths. but a few studies examined stenosis of each vessel separately. determination of stenosed coronary artery when significant ecgabnormality exists is not a difficult task. moreover, ecg abnormality is not common among cad patients. the aim of this study is to find away for specifying the lesioned vessel when there is not enough ecg changes and only based on risk factors, physical examination andpara clinic data. therefore, a new data set was used which has no missing value and includes new and effective features like functionclass, dyspnoea, q wave, st elevation, st depression and tinversion. these data was collected from 303 random visitor of tehran’sshaheed rajaei cardiovascular, medical and research centre, in 2011 fall and 2012 winter. they processed with c4.5, naïve bayes,and k-nearest neighbour (knn) algorithms and their accuracy were measured by tenfold cross validation. in the best method the accuracyof diagnosis of stenosis of each vessel reached to 74.20 ± 5.51% for left anterior descending (lad), 63.76 ± 9.73% for left circumflexand 68.33 ± 6.90% for right coronary artery. the effective features of stenosis of each vessel were found too.