Diagnosis of coronary arteries stenosis using data mining

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

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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 and Para clinic data. Therefore, a new data set was used which has no missing value and includes new and effective features like Function Class, Dyspnoea, Q Wave, ST Elevation, ST Depression and Tinversion. These data was collected from 303 random visitor of Tehran′s Shaheed Rajaei Cardiovascular, Medical and Research Centre, in 2011 fall and 2012 winter. They processed with C4.5, Nave Bayes, and k-nearest neighbour (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 and 68.33 ± 6.90% for Right Coronary Artery. The effective features of stenosis of each vessel were found too.

Original languageEnglish (US)
Pages (from-to)153-160
Number of pages8
JournalJournal of Medical Signals and Sensors
Volume2
Issue number3
DOIs
StatePublished - Jul 1 2012
Externally publishedYes

Keywords

  • C4.5 Algorithm
  • coronary artery disease
  • data mining
  • feature
  • KNN algorithm
  • Nave Bayes algorithm

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Radiological and Ultrasound Technology
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Health Informatics

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