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    Diagnosis of coronary artery disease using data mining techniques based on symptoms and ECG features

    , Article European Journal of Scientific Research ; Volume 82, Issue 4 , Aug , 2012 , Pages 542-553 ; 1450216X (ISSN) Alizadehsani, R ; Habibi, J ; Hosseini, M. J ; Boghrati, R ; Ghandeharioun, A ; Bahadorian, B ; Sani, Z. A ; Sharif University of Technology
    EuroJournals, Inc  2012
    Abstract
    The most common heart disease is Coronary artery disease (CAD). CAD is one of the main causes of heart attacks and deaths across the globe. Early diagnosis of this disease is therefore, of great importance. A large number of methods have thus far been devised for diagnosing CAD. Most of these techniques have been conducted on the basis of the Irvine dataset (University of California), which not only has a limited number of features but is also full of missing values and thus lacks reliability. The present study was designed to seek a new set, free from missing values, comprising features such as the functional class, dyspnea, Q wave, ST elevation, ST depression, and T inversion. Information...