Blockchain and Machine Learning for e-Healthcare Systems

Blockchain and Machine Learning for e-Healthcare Systems


Available for Pre-Order. This item will be available on February 26, 2021


Blockchain and machine learning technologies can mitigate healthcare issues such as slow access to medical data, poor system interoperability, lack of patient agency, and data quality and quantity for medical research. Blockchain technology facilitates and secures the storage of information in such a way that doctors can see a patient's entire medical history, but researchers see only statistical data instead of any personal information. Machine learning can make use of this data to notice patterns and give accurate predictions, providing more support for the patients and also in research related fields where there is a need for accurate data to predict credible results.

This book examines the application of blockchain technology and machine learning algorithms in various healthcare settings, covering the basics of the technologies and exploring how they can be used to improve clinical outcomes and improving the patient's experience. These topics are illustrated with reference to issues around the supply chain, drug verification, reimbursement, control access and clinical trials. Case studies are given for applications in the analysis of breast cancer, hepatitis C, and COVID-19.

Product Details

ISBN-13: 9781839531149
Publisher: Institution of Engineering and Technology (IET)
Publication date: 02/26/2021
Series: Healthcare Technologies Series
Pages: 400
Product dimensions: 6.14(w) x 9.21(h) x (d)

About the Author

Balamurugan Balusamy is a professor and chief research coordinator at Galgotias University, India. He is an eminent speaker and has given more than 275 talks. He is an IEEE brand ambassador, a book series editor for Taylor & Francis (UK) and has published more than 150 articles, 20 books and 15 patents. His research interests include but are not limited to AI, data sciences and blockchain.

Naveen Chilamkurti is a reader/associate professor and cybersecurity discipline head, Computer Science and IT, at La Trobe University, Australia. He is the inaugural editor-in-chief for International Journal of Wireless Networks and Broadband Technologies, serves on the editorial boards of several international journals and has published about 165 Journal and conference papers. His current research areas include intelligent transport systems (ITS), wireless multimedia, and wireless sensor networks, amongst others.

T. Lucia Agnes Beena is an assistant professor in the department of Information Technology, St. Joseph’s College, Tiruchirappalli, Tamil Nadu, India. She has 18 years of teaching experience and 7 years of research experience. She has published a number of research articles in Scopus indexed journals. She has authored one book and published book chapters with reputed publishers. Her areas of interest are cloud computing, big data and psychology of computer programming.

T. Poongodi is an associate professor at Galgotias University, India. Her main research areas are big data, Internet of Things, ad-hoc networks, network security and blockchain technology. She has published in more than 25 international journals, presented at national and international conferences, and authored several book chapters.

Table of Contents

  • Chapter 1: Blockchain technology and its relevance in healthcare
  • Chapter 2: Privacy issues in Blockchain
  • Chapter 3: Reforming the Traditional Business Network
  • Chapter 4: A deep dive into hyperledger
  • Chapter 5: Machine Learning
  • Chapter 6: Machine Learning in Block Chain
  • Chapter 7: Framework for approaching Blockchain in Healthcare using Machine Learning
  • Chapter 8: Reforming the Traditional Business Network
  • Chapter 9: Healthcare Analytics
  • Chapter 10: Blockchain for Healthcare
  • Chapter 11: Blockchain for E-Healthcare: An opportunity for enhancing interoperability challenges faced by block chain based applications
  • Chapter 12: Blockchain: Lifeline Care for Breast Cancer Patients in Developing Countries
  • Chapter 13: Machine Learning for Healthcare
  • Chapter 14: Machine learning in healthcare diagnosis
  • Chapter 15: Python for health care analytics made simple
  • Chapter 16: Identification and Classification of Hepatitis C Virus: An Advance Machine Learning based Approach
  • Chapter 17: Data Visualisation using Machine Learning for efficient Tracking of Pandemic - COVID-19

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