Machine Learning for Healthcare Informatics : Techniques and Applications
Language: English
Published by CRC Press Mai 2026, 2026
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Add to basketItem description from seller
Neuware - This book explores how advanced machine learning techniques are transforming healthcare, highlighting innovative applications in disease diagnosis, treatment, and healthcare management. It shows that adaptation of machine learning can bring significant benefits for the sustainability of healthcare informatics in the era of 4.0 IR.With contributions from researchers and field experts, the book covers key topics such as predictive analytics, medical image processing, and personalized healthcare. Each chapter provides detailed methodologies, datasets, and experimental results, with practical insights into AI-driven diagnostics, patient monitoring, and decision-support systems.Designed for those seeking to apply machine learning in healthcare and to advance healthcare informatics, this book is a valuable resource for researchers, professionals, and students.
Seller Inventory # 9781032643335
- Title
- Machine Learning for Healthcare Informatics : Techniques and Applications
- Author
- Mohammad Abu Yousuf
- Publisher
- CRC Press Mai 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 1032643331
- ISBN 13
- 9781032643335
- Item weight
- 676 grams
- Dimensions
- 254x178x16 mm
This book explores how advanced machine learning techniques are transforming healthcare, highlighting innovative applications in disease diagnosis, treatment, and healthcare management. It shows that adaptation of machine learning can bring significant benefits for the sustainability of healthcare informatics in the era of 4.0 IR.
With contributions from researchers and field experts, the book covers key topics such as predictive analytics, medical image processing, and personalized healthcare. Each chapter provides detailed methodologies, datasets, and experimental results, with practical insights into AI-driven diagnostics, patient monitoring, and decision-support systems.
Designed for those seeking to apply machine learning in healthcare and to advance healthcare informatics, this book is a valuable resource for researchers, professionals, and students.
"Synopsis" may belong to another edition of this title.
About the Author
Nazmul Siddique is a researcher at the School of Computing, Engineering, and Intelligent Systems, Ulster University. He has published over 170 research papers and several books on cybernetics and computational intelligence. His editorial roles in top journals highlight his academic influence and contributions.
Mohammad Shamsul Arefin is a professor at the Department of CSE, CUET, and Dean of Electrical and Computer Engineering. He has over 170 publications in journals and conferences on data mining, distributed computing, and machine learning. His leadership has significantly fostered research growth and academic excellence in many aspects.
Mohammad Abu Yousuf is currently the Vice-Chancellor of Bangabandhu Sheikh Mujibur Rahman Digital University, Bangladesh, and a Professor at the Institute of Information Technology, Jahangirnagar University. He holds a B.Sc. in Computer Science and Engineering from Shahjalal University of Science and Technology, an M.Eng. in Biomedical Engineering from Kyung Hee University, South Korea, and a Ph.D. in Science and Engineering from Saitama University, Japan. With over 125 publications in peer-reviewed journals, conferences, and book chapters, his research spans Medical Image Processing, Human-Robot Interaction, Computer Vision, and Natural Language Processing.
M. Shamim Kaiser is a professor and Chairman at the Institute of Information Technology, Jahangirnagar University. He has authored over 100 research papers on machine learning, cyber security, and cognitive radio networks. His leadership at IIT has driven academic and research excellence in ICT.
"About the title" may belong to another edition of this title.
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