Machine Learning for Healthcare

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.

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 experience in academic

teaching and has been published more than 55 times in

reputed, peer-reviewed national and international journals,

books and conferences (such as by Wiley, Taylor &

Francis, Springer, Elsevier, Science Direct, Inderscience, Annals of Computer

Science, Poland, and IEEE). His research areas include: Artificial Intelligence,

image processing, computer vision, data mining, and machine learning. He

has also been on the international conference committees of many international

conferences, and is currently serving as a reviewer for IEEE and

Inderscience journals. He has authored six internationally published books

and has edited 11 books with Wiley, IGI GLOBAL, Springer, Apple Academic

Press, CRC, and more. He is also member of various national and international

professional societies in the field of engineering and research including

being a member of IEEE, ISOC (Internet Society)); IAIP (International

Association of Innovation Professionals), ICSES (International Computer

Science and Engineering Society) IAENG (International Association of

Engineers); an associate member of IRED (Institute of Research Engineers

and Doctors; a life member of ISRD (International Society for research &

Development); and an editorial board member of IOSRD. He has received the

national Sir CV Raman lifetime achievement award in 2018 in the young

researcher and faculty category.

Pramod Singh Rathore is currently pursuing his PhD

in computer science and engineering at Bundelkhand

University and is conducting ongoing research on networking.

He has an M. Tech in computer science and

engineering from Government Engineering College

Ajmer, at Rajasthan Technical University, Kota, India.

He has been working as an Assistant Professor of the

Computer Science and Engineering Department at

Aryabhatt Engineering College and Research Centre,

Ajmer, Rajasthan and is also a visiting faculty at Government University

MDS Ajmer. He has over eight years of experience in academic teaching

and has been published more than 45 times in reputed, peer-reviewed

national and international journals, books and conferences (such as Wiley,

IGI GLOBAL, Taylor & Francis, Springer, Elsevier, Science Direct, Annals of

Computer Science, Poland, and IEEE). He has co-authored and edited many

books with many reputed publishers like Wiley, and CRC Press, USA. His

research areas include: NS2, computer networks, mining, and DBMS.

Dac-Nhuong Le has a PhD and is Deputy-Head of

the Faculty of Information Technology at Haiphong

University, Vietnam and Vice-Director of Information

Technology at the Apply Center of the same university.

He is a research scientist at the Research and

Development Center of Visualization & Simulation in

(CSV), Duy Tan University, Danang, Vietnam. He has

more than 45 publications in the reputed international

conferences, journals, and online book chapter contributions

(indexed by: SCI, SCIE, SSCI, Scopus, ACM, and

DBLP). His areas of research include: evaluation computing and approximate

algorithms, network communication, security and vulnerability,

network performance analysis and simulation, cloud computing, and biomedical

image processing. His core work is in network security, wireless,

soft computing, mobile computing and biomedical technology. Recently, he

has been on a technical program committee, a technical reviewer, and the

track chair for international conferences such as: FICTA 2014, CSI 2014, IC4SD

2015, ICICT 2015, INDIA 2015, IC3T 2015, INDIA 2016, FICTA 2016, IC3T 2016,

ICDECT 2016, IUKM 2016, INDIA 2017, FICTA 2017, CISC 2017, ICICC 2018,

ICCUT 2018 under the Springer-ASIC/LNAI/CISC Series. Presently, he is

serving on the editorial board of international journals and he has authored

six computer science books (published by Springer, Wiley, CRC Press,

Lambert Publication, VSRD Academic Publishing, and Scholar Press).