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 artificial intelligence in cardiology Coverage of data mining classification techniques, applied machine learning and advanced methods for estimating HF severity and diagnosing and predicting heart failure Discussion of the risks and issues associated with the remote monitoring system Assessment of the potential applications and future of implantable and wearable devices in heart failure prediction and detection Artificial intelligence in mobile monitoring technologies to provide clinicians with improved treatment options, ultimately easing access to healthcare by all patient populations. Providing the latest research data for the diagnosis and treatment of heart failure, Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods is an excellent resource for nurses, nurse practitioners, physician assistants, medical students, and general practitioners to gain a better understanding of bedside cardiology.

PREDICTING HEART FAILUREPredicting 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 artificial intelligence in cardiology* Coverage of data mining classification techniques, applied machine learning and advanced methods for estimating HF severity and diagnosing and predicting heart failure* Discussion of the risks and issues associated with the remote monitoring system* Assessment of the potential applications and future of implantable and wearable devices in heart failure prediction and detection* Artificial intelligence in mobile monitoring technologies to provide clinicians with improved treatment options, ultimately easing access to healthcare by all patient populations.Providing the latest research data for the diagnosis and treatment of heart failure, Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods is an excellent resource for nurses, nurse practitioners, physician assistants, medical students, and general practitioners to gain a better understanding of bedside cardiology.

Preface viiAbbreviations ixAcknowledgment xvii1 Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods for Prediction of Heart Failure 1Hidayet Takci2 Conventional Clinical Methods for Predicting Heart Disease 23Aisha A-Mohannadi, Jayakanth Kunhoth, Al Anood Najeeb, Somaya Al-Maadeed, and Kishor Kumar Sadasivuni3 Types of Biosensors and their Importance in Cardiovascular Applications 47S Irem Kaya, Leyla Karadurmus, Ahmet Cetinkaya, Goksu Ozcelikay, and Sibel A Ozkan4 Overview and Challenges of Wireless Communication and Power Transfer for Implanted Sensors 81Mohamed Zied Chaari and Somaya Al-Maadeed5 Minimally Invasive and Non-Invasive Sensor Technologies for Predicting Heart Failure: An Overview 109Huseyin Enes Salman, Mahmoud Khatib A.A Al-Ruweidi, Hassen M Ouakad, and Huseyin C Yalcin6 Artificial Intelligence Techniques in Cardiology: An Overview 139Ikram-Ul Haq and Bo Xu7 Utilizing Data Mining Classification Algorithms for Early Diagnosis of Heart Diseases 155Ahmad Mousa Altamimi and Mohammad Azzeh8 Applications of Machine Learning for Predicting Heart Failure 171Sabri Boughorbel, Yassine Himeur, Huseyin Enes Salman, Faycal Bensaali,Faisal Farooq, and Huseyin C Yalcin9 Machine Learning Techniques for Predicting and Managing Heart Failure 189Dafni 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 Fotiadis10 Clinical Applications of Artificial Intelligence in Early and Accurate Detection of Low- Concentration CVD Biomarkers 227Meena Laad, Sajna M.S, Kishor Kumar Sadasivuni, and Sadiya Waseem11 Commercial Non-Invasive and Invasive Devices for Heart Failure Prediction: A Review 243Jayakanth Kunhoth, Nandhini Subramanian, and Ahmed Bouridane12 Artificial Intelligence Based Commercial Non-Invasive and Invasive Devices for Heart Failure Diagnosis and Prediction 269Kanchan Kulkarni, Eric M Isselbacher, and Antonis A Armoundas13 Future Techniques and Perspectives on Implanted and Wearable Heart Failure Detection Devices 295Muhammad E.H Chowdhury, Amith Khandaker, Yazan Qiblawey, Fahmida Haque, Maymouna Ezeddin, Tawsifur Rahman, Nabil Ibtehaz, and Khandaker Reajul IslamIndex 321