Machine Learning for Healthcare Applications - Hardcover

Über die Autorin bzw. den Autor

Sachi Nandan Mohanty received his PhD from IIT Kharagpur in 2015. He has recently joined as an associate professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education Hyderabad. His research areas include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, and computational intelligence. He has published 20 SCI journal articles and has authored/edited 7 books.

G. Nalinipriya is a professor in the Department of Information Technology, Anna University, Chennai where she also obtained her PhD. She has more than 23 years of experience in the field of teaching, industry and research and her interests include artificial intelligence, machine learning, data science and cloud security.

Om Prakash Jena is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha. He has 10 years of teaching and research experience and has published several technical papers in international journals/conferences/edited books. His current research interests include pattern recognition, cryptography, network security, soft computing, data analytics and machine automation.

Achyuth Sarkar received his PhD in Computer Science and Engineering from the National Institute of Technology, Arunachal Pradesh in 2019. He has teaching experience of more than 10 years.

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This book elucidates different dimensions of machine learning applications and illustrates its use in solutions of assorted real world biomedical and healthcare problems.

Machine learning is one of the principal components of computational methodology. In today’s highly integrated world, when solutions to problems are cross-disciplinary in nature, machine learning promises to become a powerful means for obtaining solutions to problems very quickly, yet accurately and acceptably.

The approach of this book is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. Since this book is intended to be useful to a wide audience, it contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning.

Machine Learning for Healthcare Applications comprises 22 real-time innovative chapters providing a comprehensive overview of current technology. Each of these chapters specifies requirements and provides a description of both the chosen approach and its implementation.

Audience

The book will be read by scientists and engineers in artificial intelligence, information technology, bioinformatics as well as specialist stakeholders in the biomedical sector such as hospitals & healthcare providers, pharmaceutical & biotechnology companies, medical imaging & diagnostics centers, healthcare assistance robots manufacturers, telehealth companies.

Aus dem Klappentext

This book elucidates different dimensions of machine learning applications and illustrates its use in solutions of assorted real world biomedical and healthcare problems.

Machine learning is one of the principal components of computational methodology. In today’s highly integrated world, when solutions to problems are cross-disciplinary in nature, machine learning promises to become a powerful means for obtaining solutions to problems very quickly, yet accurately and acceptably.

The approach of this book is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. Since this book is intended to be useful to a wide audience, it contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning.

Machine Learning for Healthcare Applications comprises 22 real-time innovative chapters providing a comprehensive overview of current technology. Each of these chapters specifies requirements and provides a description of both the chosen approach and its implementation.

Audience

The book will be read by scientists and engineers in artificial intelligence, information technology, bioinformatics as well as specialist stakeholders in the biomedical sector such as hospitals & healthcare providers, pharmaceutical & biotechnology companies, medical imaging & diagnostics centers, healthcare assistance robots manufacturers, telehealth companies.