Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems

Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems by Om Prakash Jena, Bharat Bhushan, Nitin Rakesh, Parma Nand Astya, Yousef Farhaoui

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Description This book describes the fundamental concepts of machine learning and deep learning techniques in a healthcare system. The aim of this book is to describe how deep learning methods are used to insure high quality data processing, medical image and signal analysis, and improved healthcare application.

Publisher Description

The goal of medical informatics is to improve life expectancy, disease diagnosis and quality of life. Medical devices have revolutionized healthcare and have led to the modern age of machine learning, deep learning and Internet of Medical Things (IoMT) with their proliferation, mobility and agility. This book exposes different dimensions of applications for computational intelligence and explains its use in solving various biomedical and healthcare problems in the real world. This book describes the fundamental concepts of machine learning and deep learning techniques in a healthcare system. The aim of this book is to describe how deep learning methods are used to ensure high-quality data processing, medical image and signal analysis and improved healthcare applications. This book also explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems. Furthermore, it provides the healthcare sector with innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modelling, advanced deployment, case studies, analytical results, computational structuring and significant progress in the field of machine learning and deep learning in healthcare applications.FEATURES Explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems Provides guidance in developing intelligence-based diagnostic systems, efficient models and cost-effective machines Provides the latest research findings, solutions to the concerning issues and relevant theoretical frameworks in the area of machine learning and deep learning for healthcare systems Describes experiences and findings relating to protocol design, prototyping, experimental evaluation, real testbeds and empirical characterization of security and privacy interoperability issues in healthcare applications Explores and illustrates the current and future impacts of pandemics and mitigates risk in healthcare with advanced analyticsThis book is intended for students, researchers, professionals and policy makers working in the fields of public health and in the healthcare sector. Scientists and IT specialists will also find this book beneficial for research exposure and new ideas in the field of machine learning and deep learning.

Author Biography

Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, and Odisha. Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at School of Engineering and Technology, Sharda University, Greater Noida, India. Dr. Nitin Rakesh is the Head of Computer Science & Engineering Department for B.Tech/M.Tech (CSE/IT), B.Tech CSE-IBM Specializations, B.Tech CSE-I Nurture, BCA/MCA, BSc/MSc-CS at School of Engineering and Technology,at Sharda University, India. Dr. Parma Nand is a Dean, School of Engineering Technology, Sharda University Greater Noida. Dr. Yousef Farhaoui is a Professor at Moulay Ismail University, Faculty of Sciences and Techniques, Morocco.

Details ISBN 1032037954 ISBN-13 9781032037950 Title Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems Author Om Prakash Jena, Bharat Bhushan, Nitin Rakesh, Parma Nand Astya, Yousef Farhaoui Format Paperback Year 2024 Pages 379 Publisher Taylor & Francis Ltd GE_Item_ID:160477595; About Us

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