Diagnosis of Coronary Arteries Stenosis Using Data Mining

Roohallah Alizadehsani, Jafar Habibi, Behdad Bahadorian, Hoda Mashayekhi, Asma Ghandeharioun, Reihane Boghrati, Zahra Alizadeh Sani

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Abstract


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 very essential and is an important field of medical studies. Many methods are used to diagnose CAD so far. These methods reduce cost and deaths. But a few studies examined stenosis of each vessel separately. Determination of stenosed coronary artery when significant ECG abnormality exists is not a difficult task. Moreover, ECG abnormality is not common among CAD patients. The aim of this study is to find a way for specifying the lesioned vessel when there is not enough ECG changes and only based on risk factors, physical examination & paraclinic data. Therefore,  a new data set was used which has no missing value and includes  new and effective features like Function Class, Dyspnea, Q Wave, ST Elevation, ST Depression and Tinversion. These data were collected from 303 random visitor of Tehran’s Shaheed Rajaei Cardiovascular, Medical and Research Center, in 2011 fall and 2012 winter. They processed with C4.5, Naïve Bayes, and k-nearest neighbour algorithm (KNN) algorithms and their accuracy were measured by tenfold cross validation. In the best method the accuracy of diagnosis of stenosis of each vessel reached to 74.20 5.51% for Left Anterior Descending (LAD), 63.76 9.73% for Left Circumflex (LCX) and 68.33 6.90% for Right Coronary Artery (RCA). The effective features of stenosis of each vessel were found too.

Keywords


Data mining; Feature; Coronary Artery Disease; Naïve Bayes Algorithm; C4.5 Algorithm; KNN Algorithm

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