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Artificial Intelligence and Data Mining Methods for Cardiovascular Risk Prediction

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Cardiovascular Computing—Methodologies and Clinical Applications

Part of the book series: Series in BioEngineering ((SERBIOENG))

Abstract

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.

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Abbreviations

ATS:

Atherosclerosis

AUC:

Area Under the ROC Curve

BMI:

Body Mass Index

CA:

Coronary Angiography

CAD:

Coronary Artery Disease

CART:

Classification and Regression Trees

CFS:

Correlation-based Feature Selection

CTA:

Computed Tomography Angiography

CVD:

Cardiovascular Disease

DBN:

Dynamic Bayesian Network

EHR:

Electronic Health Record

FFNN:

Feed-forward Neural Network

FRS:

Framingham Risk Score

FURIA:

Fuzzy Unordered Rule Induction Algorithm

GAM:

Generalized Additive Model

GBT:

Gradient Boosted Trees

HDL:

High-density Lipoprotein

IVUS:

Intravascular Ultrasound

LAD:

Left Anterior Descending

LCX:

Left Circumflex

LDL:

Low-density Lipoprotein

LR:

Logistic Regression

MRI:

Magnetic Resonance Imaging

NPV:

Negative Predictive Value

OCT:

Optical Coherence Tomography

PHR:

Personal Health Record

PPV:

Positive Predictive Value

RBF:

Radial Basis Function Network

RCA:

Right Coronary Artery

RF:

Random Forest

ROC:

Receiver Operating Curve

RTF:

Rotation Forest

SMOTE:

Synthetic Minority Oversampling Technique

SOFM:

Self-organizing Feature Map

SVM:

Support Vector Machine

TA:

Temporal Abstraction

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Acknowledgements

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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Authors and Affiliations

  1. Unit of Medical Technology and Intelligent Information Systems, Materials Science and Engineering Department, University of Ioannina, Ioannina, 45110, Greece

    Eleni I. Georga, Vassiliki I. Kigka, Themis P. Exarchos & Dimitrios I. Fotiadis

  2. Biomedical Research Department, FORTH, Institute of Molecular Biology and Biotechnology, Ioannina, 45110, Greece

    Nikolaos S. Tachos, Antonis I. Sakellarios, Vassiliki I. Kigka & Dimitrios I. Fotiadis

  3. Institute of Clinical Physiology, National Research Council, Pisa, 56124, Italy

    Gualtiero Pelosi & Oberdan Parodi

  4. Department of Cardiology, Medical School, University of Ioannina, Ioannina, 45110, Greece

    Lampros K. Michalis

Authors
  1. Eleni I. Georga
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  2. Nikolaos S. Tachos
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  3. Antonis I. Sakellarios
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  4. Vassiliki I. Kigka
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  5. Themis P. Exarchos
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  6. Gualtiero Pelosi
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  9. Dimitrios I. Fotiadis
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Corresponding author

Correspondence to Dimitrios I. Fotiadis .

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Editors and Affiliations

  1. Medical School, National Kapodistrian University of Athens, Athens, Greece

    Spyretta Golemati

  2. Biomedical Simulations and Imaging Laboratory, National Technical University of Athens, Athens, Greece

    Konstantina S. Nikita

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Georga, E.I. et al. (2019). Artificial Intelligence and Data Mining Methods for Cardiovascular Risk Prediction. In: Golemati, S., Nikita, K. (eds) Cardiovascular Computing—Methodologies and Clinical Applications. Series in BioEngineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-5092-3_14

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