Description

Book Synopsis
PREDICTING HEART FAILURE

Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods focuses on the mechanics and symptoms of heart failure and various approaches, including conventional and modern techniques to diagnose it.

This book also provides a comprehensive but concise guide to all modern cardiological practice, emphasizing practical clinical management in many different contexts. Predicting Heart Failure supplies readers with trustworthy insights into all aspects of heart failure, including essential background information on clinical practice guidelines, in-depth, peer-reviewed articles, and broad coverage of this fast-moving field. Readers will also find:

  • Discussion of the main characteristics of cardiovascular biosensors, along with their open issues for development and application
  • Summary of the difficulties of wireless sensor communication and power transfer, and the utility of a

    Table of Contents

    Preface vii

    Abbreviations ix

    Acknowledgment xvii

    1 Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods for Prediction of Heart Failure 1
    Hidayet Takcı

    2 Conventional Clinical Methods for Predicting Heart Disease 23
    Aisha A-Mohannadi, Jayakanth Kunhoth, Al Anood Najeeb, Somaya Al-Maadeed, and Kishor Kumar Sadasivuni

    3 Types of Biosensors and their Importance in Cardiovascular Applications 47
    S Irem Kaya, Leyla Karadurmuş, Ahmet Cetinkaya, Goksu Ozcelikay, and Sibel A Ozkan

    4 Overview and Challenges of Wireless Communication and Power Transfer for Implanted Sensors 81
    Mohamed Zied Chaari and Somaya Al-Maadeed

    5 Minimally Invasive and Non-Invasive Sensor Technologies for Predicting Heart Failure: An Overview 109
    Huseyin Enes Salman, Mahmoud Khatib A.A Al-Ruweidi, Hassen M Ouakad, and Huseyin C Yalcin

    6 Artificial Intelligence Techniques in Cardiology: An Overview 139
    Ikram-Ul Haq and Bo Xu

    7 Utilizing Data Mining Classification Algorithms for Early Diagnosis of Heart Diseases 155
    Ahmad Mousa Altamimi and Mohammad Azzeh

    8 Applications of Machine Learning for Predicting Heart Failure 171
    Sabri Boughorbel, Yassine Himeur, Huseyin Enes Salman, Faycal Bensaali,Faisal Farooq, and Huseyin C Yalcin

    9 Machine Learning Techniques for Predicting and Managing Heart Failure 189
    Dafni K Plati, Evanthia E Tripoliti, Georgia S Karanasiou, Aidonis Rammos,Aris Bechlioulis, Chris J Watson, Ken McDonald, Mark Ledwidge, Yorgos Goletsis, Katerina K Naka, and Dimitrios I Fotiadis

    10 Clinical Applications of Artificial Intelligence in Early and Accurate Detection of Low- Concentration CVD Biomarkers 227
    Meena Laad, Sajna M.S, Kishor Kumar Sadasivuni, and Sadiya Waseem

    11 Commercial Non-Invasive and Invasive Devices for Heart Failure Prediction: A Review 243
    Jayakanth Kunhoth, Nandhini Subramanian, and Ahmed Bouridane

    12 Artificial Intelligence Based Commercial Non-Invasive and Invasive Devices for Heart Failure Diagnosis and Prediction 269
    Kanchan Kulkarni, Eric M Isselbacher, and Antonis A Armoundas

    13 Future Techniques and Perspectives on Implanted and Wearable Heart Failure Detection Devices 295
    Muhammad E.H Chowdhury, Amith Khandaker, Yazan Qiblawey, Fahmida Haque, Maymouna Ezeddin, Tawsifur Rahman, Nabil Ibtehaz, and Khandaker Reajul Islam

    Index 321

Predicting Heart Failure

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    £999.99

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    A Hardback by Kishor Kumar Sadasivuni, Hassen M. Ouakad, Somaya Al-Maadeed

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      Publisher: John Wiley & Sons Inc
      Publication Date: 28/04/2022
      ISBN13: 9781119813019, 978-1119813019
      ISBN10: 1119813018

      Description

      Book Synopsis
      PREDICTING HEART FAILURE

      Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods focuses on the mechanics and symptoms of heart failure and various approaches, including conventional and modern techniques to diagnose it.

      This book also provides a comprehensive but concise guide to all modern cardiological practice, emphasizing practical clinical management in many different contexts. Predicting Heart Failure supplies readers with trustworthy insights into all aspects of heart failure, including essential background information on clinical practice guidelines, in-depth, peer-reviewed articles, and broad coverage of this fast-moving field. Readers will also find:

      • Discussion of the main characteristics of cardiovascular biosensors, along with their open issues for development and application
      • Summary of the difficulties of wireless sensor communication and power transfer, and the utility of a

        Table of Contents

        Preface vii

        Abbreviations ix

        Acknowledgment xvii

        1 Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods for Prediction of Heart Failure 1
        Hidayet Takcı

        2 Conventional Clinical Methods for Predicting Heart Disease 23
        Aisha A-Mohannadi, Jayakanth Kunhoth, Al Anood Najeeb, Somaya Al-Maadeed, and Kishor Kumar Sadasivuni

        3 Types of Biosensors and their Importance in Cardiovascular Applications 47
        S Irem Kaya, Leyla Karadurmuş, Ahmet Cetinkaya, Goksu Ozcelikay, and Sibel A Ozkan

        4 Overview and Challenges of Wireless Communication and Power Transfer for Implanted Sensors 81
        Mohamed Zied Chaari and Somaya Al-Maadeed

        5 Minimally Invasive and Non-Invasive Sensor Technologies for Predicting Heart Failure: An Overview 109
        Huseyin Enes Salman, Mahmoud Khatib A.A Al-Ruweidi, Hassen M Ouakad, and Huseyin C Yalcin

        6 Artificial Intelligence Techniques in Cardiology: An Overview 139
        Ikram-Ul Haq and Bo Xu

        7 Utilizing Data Mining Classification Algorithms for Early Diagnosis of Heart Diseases 155
        Ahmad Mousa Altamimi and Mohammad Azzeh

        8 Applications of Machine Learning for Predicting Heart Failure 171
        Sabri Boughorbel, Yassine Himeur, Huseyin Enes Salman, Faycal Bensaali,Faisal Farooq, and Huseyin C Yalcin

        9 Machine Learning Techniques for Predicting and Managing Heart Failure 189
        Dafni K Plati, Evanthia E Tripoliti, Georgia S Karanasiou, Aidonis Rammos,Aris Bechlioulis, Chris J Watson, Ken McDonald, Mark Ledwidge, Yorgos Goletsis, Katerina K Naka, and Dimitrios I Fotiadis

        10 Clinical Applications of Artificial Intelligence in Early and Accurate Detection of Low- Concentration CVD Biomarkers 227
        Meena Laad, Sajna M.S, Kishor Kumar Sadasivuni, and Sadiya Waseem

        11 Commercial Non-Invasive and Invasive Devices for Heart Failure Prediction: A Review 243
        Jayakanth Kunhoth, Nandhini Subramanian, and Ahmed Bouridane

        12 Artificial Intelligence Based Commercial Non-Invasive and Invasive Devices for Heart Failure Diagnosis and Prediction 269
        Kanchan Kulkarni, Eric M Isselbacher, and Antonis A Armoundas

        13 Future Techniques and Perspectives on Implanted and Wearable Heart Failure Detection Devices 295
        Muhammad E.H Chowdhury, Amith Khandaker, Yazan Qiblawey, Fahmida Haque, Maymouna Ezeddin, Tawsifur Rahman, Nabil Ibtehaz, and Khandaker Reajul Islam

        Index 321

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