Artificial Intelligence and Data Mining Methods for Cardiovascular Risk Prediction

  • Eleni I. Georga
  • Nikolaos S. Tachos
  • Antonis I. Sakellarios
  • Vassiliki I. Kigka
  • Themis P. Exarchos
  • Gualtiero Pelosi
  • Oberdan Parodi
  • Lampros K. Michalis
  • Dimitrios I. FotiadisEmail author
Part of the Series in BioEngineering book series (SERBIOENG)


This chapter describes the state-of-the-art in artificial intelligence and machine learning methods for cardiovascular disease diagnosis and prognosis, focusing on Coronary Artery Disease (CAD). We aim at providing a cohesive overview of the existing methodologies in the topic and the most exploitable predictors for CAD staging and evolution. Thus, the relevant literature is analysed and contrasted with respect to the acquired dataset, the examined feature space, the employed predictive modelling schemes and their discriminative or predictive capacity. Moreover, important challenges stemming from the increasing ubiquity of electronic health records, personal health records and big data are discussed and, given the limitations of current approaches, future directions are delineated.


Machine learning Artificial intelligence Cardiovascular disease Coronary artery disease Atherosclerosis Diagnosis Prediction 

List of Abbreviations




Area Under the ROC Curve


Body Mass Index


Coronary Angiography


Coronary Artery Disease


Classification and Regression Trees


Correlation-based Feature Selection


Computed Tomography Angiography


Cardiovascular Disease


Dynamic Bayesian Network


Electronic Health Record


Feed-forward Neural Network


Framingham Risk Score


Fuzzy Unordered Rule Induction Algorithm


Generalized Additive Model


Gradient Boosted Trees


High-density Lipoprotein


Intravascular Ultrasound


Left Anterior Descending


Left Circumflex


Low-density Lipoprotein


Logistic Regression


Magnetic Resonance Imaging


Negative Predictive Value


Optical Coherence Tomography


Personal Health Record


Positive Predictive Value


Radial Basis Function Network


Right Coronary Artery


Random Forest


Receiver Operating Curve


Rotation Forest


Synthetic Minority Oversampling Technique


Self-organizing Feature Map


Support Vector Machine


Temporal Abstraction



This work is partially funded by the European Commission: Project SMARTOOL, “Simulation Modeling of coronary ARTery disease: a tool for clinical decision support—SMARTool” GA number: 689068.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Eleni I. Georga
    • 1
  • Nikolaos S. Tachos
    • 2
  • Antonis I. Sakellarios
    • 2
  • Vassiliki I. Kigka
    • 1
    • 2
  • Themis P. Exarchos
    • 1
  • Gualtiero Pelosi
    • 3
  • Oberdan Parodi
    • 3
  • Lampros K. Michalis
    • 4
  • Dimitrios I. Fotiadis
    • 1
    • 2
    Email author
  1. 1.Unit of Medical Technology and Intelligent Information Systems, Materials Science and Engineering DepartmentUniversity of IoanninaIoanninaGreece
  2. 2.Biomedical Research DepartmentFORTH, Institute of Molecular Biology and BiotechnologyIoanninaGreece
  3. 3.Institute of Clinical Physiology, National Research CouncilPisaItaly
  4. 4.Department of Cardiology, Medical SchoolUniversity of IoanninaIoanninaGreece

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