CRC Press Machine Learning for Healthcare: Handling and Managing Data

Product Description Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors. About the Author Rashmi Agrawal is a Professor in the Department of Computer Applications in MRIIRS, Faridabad. Dr. Agrawal has a rich teaching experience of more than 17 years. She is UGC-NET(CS) qualified, and has completed a PhD, M. Phil, M. Tech, MSc, and MBA(IT). Her PhD focused on the area of machine learning, and her areas of expertise include Artificial Intelligence, Machine Learning, Data Mining, and Operating Systems. She has published more than 30 research papers in various national and international conferences and journals, and has authored many published books and chapters. She has organized various faculty development programs and has also directly participated in workshops and faculty development programmes. She is actively involved in research activities, and is a lifetime member of Computer Society of India. She has been a member of the technical programme committee of various reputable conferences.   Jyotir Moy Chatterjee is an Assistant Professor at the IT Department of Lord Buddha Education Foundation (Asia Pacific University of Technology and Innovation), Kathmandu, Nepal. Prior to this he has worked as an Assistant Professor at the CSE Department of GD Rungta College of Engineering and Technology (CSVTU), Bhilai, India. He has completed an M. Tech in Computer Science and Engineering from Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha and a B. Tech in Computer Science and Engineering from Dr. MGR Educational and Research Institute, Chennai. He has published 40 international research papers, two international conference papers, authored four books, edited eight books, written 11 book chapters and has one patent to his account. His research interests include cloud computing, big data, privacy preservation, data mining, the Internet of Things, machine learning, and blockchain technology. He is a member of various professional societies and international conferences.   Abhishek Kumar has a PhD in computer science from University of Madras and an M. Tech in computer science and engineering from Government Engineering College Ajmer at Rajasthan Technical University, Kota, India. He has over eight years of e