Predicting Heart Failure. Группа авторов

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with Mid-range or Mildly Reduced EF, 5HFpEFHeart Failure with Preserved Ejection Fraction, 5HFrEFHeart Failure with Reduced Ejection Fraction, 5HRVHeart Rate Variability, 9K-NNK-nearest Neighbor, 8LGELate Gadolinium Enhancement, 6LMTLogistic Model Trees, 31LRLogistic Regression, 8LS-SVMLeast Square SVM, 9LSTMLong Short-term Memory, 9MAGGICMeta-analysis Global Group in Chronic, 24MLMachine Learning, 7MLPMultilayer Perceptron, 8NBNaïve Bayes, 34NLPNatural Language Processing, 16NNNeural Network, 8NRDNationwide Readmissions Database, 21NYHANew York Heart Association, 5RBFRadial Basis Function, 18RDWRed Blood Cell Distribution Width, 26RFRandom Forest, 9RNNRecurrent Neural Network, 8ROTRotation Forest, 16RSRough Set, 8RSARandom Search Algorithm, 10SAESparse Auto-encoder, 9SGDStochastic Gradient Descent, 9SVMSupport Vector Machines, 8

       Chapter 10

AIArtificial Intelligence, 1 bp Blood Pressure, 11
CADCoronary Artery Disease, 15
CTComputed Tomography, 15
CVDsCardiovascular Diseases, 1
DLDeep Learning, 12
ECGElectrocardiogram, 1
EISElectrochemical Impedance Spectroscopy, 4
IoTThe Internet of Things, 11
MRIMagnetic Resonance Imaging, 2,13
RIRefractive Index, 6

       Chapter 11

BMIBody Mass Index, 9
CHFChronic Heart Failures, 2
CIHMChronicle Implantable Hemodynamic Monitor, 19
CRTCardiac Resynchronization Therapy, 19
HCGHuman Chorionic Gonadotropin, 21
IASDInter Atrial Shunt Device, 22
LALeft Atrial, 17
LAPLeft Atrial Pressure, 18
MCTMobile Cardiac Telemetry, 6
NYHANew York Heart Association, 19
PAPulmonary Artery, 17
PAMPatient Advisory Module, 20
RVRight Ventricle, 17

       Chapter 12

AIArtificial Intelligence, 3
ANNArtificial Neural Networks, 4
AUCArea Under Curve, 6
CNNConvolutional Neural Network, 4
CRTCardiac Resynchronization Therapy, 6
DLDeep Learning, 3
DNNDeep Neural Network, 5
ECGElectrocardiographic, 5
Heart Failure, 1
k-NNk-Nearest Neighbors, 5
LVADLeft Ventricular Assist Device, 7
MLMachine Learning, 3
PPGsPhotoplethysmograms, 8
RFRandom Forest, 4
RNNRecurrent Neural Network, 5
RVRight Ventricular, 2
RVFRight-ventricular Failure, 7
RVFRSRight Ventricular Failure Risk Score, 7
SVMSupport Vector Machine, 4

       Chapter 13

ABPArterial Blood Pressure, 4
AFAtrial Fibrillation, 11
CADCoronary Artery Diseases, 19
CardioMEMSCardio-Microelectromechanical system, 3
CRTCardiac Resynchronization Therapy, 18
CRT-DCardiac Resynchronization Therapy Defibrillator, 4
CVDsCardiovascular Diseases, 3
ECGElectrocardiogram, 2
FDAFood and Drug Administration, 13
HFHeart Failure, 1
hourHeart Rate, 13
ICDsImplantable Cardioverter-defibrillators, 5
LAPLeft Atrial Pressure, 5
LVADLeft Ventricular Assist Device, 18
MIMyocardial Infarction, 14
NSTEMINon-ST-elevation Myocardial Infarction, 18
NYHANew York Heart Association, 10
OHRMOptical Heart Rate Monitor, 9
PAPPulmonary Artery Pressure, 3
PDPhotodiode, 9
PPGPhotoplethysmogram, 4
RRRespiration Rate, 13
STEMIST-elevation Myocardial Infarction, 18
SVMSupport Vector Machine, 18
VFVentricular Fibrillation, 11

       Hidayet Takcı

      1.1 Introduction

      Heart diseases are the deadliest in the world. Of the many diseases included in the category of heart disease, the most prominent is coronary artery disease (CAD), which causes heart attacks. CAD, high blood pressure, and many other heart diseases cause heart failure (HF). With HF being a consequence of heart disease, the prediction of HF is related to the prediction of diseases categorized as heart disease.

      In this chapter, the diagnosis of HF is discussed in terms of invasive/non-invasive and artificial intelligence/machine learning techniques. Invasive and non-invasive techniques are distinct in the way the patient is treated. Invasive methods are usually associated with a physical intervention in the body. This intervention involves operations such as taking blood for blood analysis and not pressing strongly on the abdominal area. Non-invasive methods include physical therapy, taking blood pressure, and temperature measurement. In today’s world, where information technologies have evolved in every field, the field of health has also received its share. Computer-aided clinical decision support systems provide the strongest support for diagnostic studies today. The most important components of computer-aided diagnosis are artificial intelligence and machine learning systems that offer a wide range of services from smart assistant applications to imaging techniques. Artificial intelligence and machine learning have a healing role in electrocardiography, echocardiography, and similar invasive and non-invasive techniques.

      1.2 Heart Failure

      HF belongs to a class of diseases that occur due to several diseases known as heart diseases and can be fatal if left untreated. In this section, HF is defined and the factors causing it, its symptoms, and its treatment are examined.

      1.2.1

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