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A Novel Effective Ensemble Model for Early Detection of Coronary Artery Disease

  • Zahia Aouabed
  • Moloud AbdarEmail author
  • Nadia Tahiri
  • Jaël Champagne Gareau
  • Vladimir Makarenkov
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)

Abstract

One of the major types of cardiovascular diseases is Coronary Artery Disease (CAD). This study tackles the problem of CAD detection using a new accurate hybrid machine learning model. The proposed ensemble model combines several classical machine learning techniques. Our base algorithm is used with four different kernel functions (linear, polynomial, radial basis and sigmoid). The new model was applied to analyze the well-known Cleveland CAD dataset from the UCI repository. To improve the performance of the model, we first selected the most important features of this dataset using a genetic search algorithm. Second, we applied a multi-level filtering technique to balance the data using the ClassBalancer and Resample methods. Our model provided the average CAD prediction accuracy of 98.34% for the Cleveland data (the average was taken over the four kernel functions).

Keywords

Machine learning Data mining Coronary Artery Disease Ensemble learning Nested Ensemble 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zahia Aouabed
    • 1
  • Moloud Abdar
    • 1
    Email author
  • Nadia Tahiri
    • 1
  • Jaël Champagne Gareau
    • 1
  • Vladimir Makarenkov
    • 1
  1. 1.Department of Computer ScienceUniversity of Quebec in MontrealMontrealCanada

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