Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods

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.

About the Editors

Dr Kishor Kumar Sadasivuni, Center for Advanced Materials, Qatar University, Qatar

Dr Hassen M. Ouakad, Department of Mechanical and Industrial Engineering, Sultan Qaboos University, Oman

Prof. Somaya Al-Maadeed, Department of Computer Science and Engineering, Qatar University, Qatar

Dr Huseyin C. Yalcin, Biomedical Research Center, Qatar University, Qatar

Dr Issam Bait Bahadur, Department of Mechanical and Industrial Engineering, Sultan Qaboos University, Oman

This publication was supported by Qatar University Internal Grant No. IRCC-2020-013 and Sultan Qaboos University through Grant # CL/SQU-QU/ENG/20/01, respectively. The findings achieved herein are solely the responsibility of the authors.