Our knowledge of human biology especially related to the heart, increases every day. This makes it nearly impossible for physicians to stay current on the latest research in their fields, let alone in all of the others that directly affect their ability to treat their patients properly. 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. Moreover, it book provides a detailed presentation of the latest research data for preventing and treating heart failure. In this book, thirteen chapters address different conditions related to the heart, with detailed descriptions of each. The first chapter discusses invasive, non-invasive, machine learning, and artificial intelligence-based methods for predicting heart failure. Additionally, this chapter discusses heart failure causes, symptoms, and treatment, as well as research related to heart failure. In the second chapter, we examine the traditional methods of predicting heart diseases and implementing artificial intelligence technology to predict heart diseases accurately. A discussion of the main characteristics of cardiovascular biosensors is presented in Chapter 3, along with their open issues for development and application. We summarize the difficulties of wireless sensor communication and power transfer in chapters four, five, and six, which outline the utility of artificial intelligence in cardiology. Chapter 7 discusses how to predict heart diseases using data mining classification techniques. Applied machine learning is all discussed in Chapters 8 and 9 and advanced methods for estimating HF severity and diagnosing and predicting heart failure. In chapter 10, the present state of artificial intelligence and biosensors based on materials is briefly discussed. The underlying technologies of various invasive and non-invasive devices, and their benefits, are discussed and analyzed in Chapter 11. A discussion of the risks and issues associated with the remote monitoring system was also included in this chapter. A panel of these HF prediction devices is presented in Chapter 12 and their invasive and noninvasive alternatives. Furthermore, it advances the potential of artificial intelligence in mobile monitoring technologies to provide clinicians with improved treatment options, ultimately easing access to healthcare by all patient populations. Chapter 13 assessed the potential applications of implantable and wearable devices in HF detection application, summarizes available data for wearables, and machine learning for improving patients' cardiac health, and discusses the future of wearables for early prediction of HF. Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods provides a comprehensive but concise guide to all modern cardiological practice, emphasizing practical clinical management in many different contexts. This book provides 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. Providing the latest research data for the diagnosis and treatment of heart failure, this book is an excellent resource for nurses, nurse practitioners, physician assistants, medical students, and general practitioners to gain a better understanding of bedside cardiology.