Unlocking the Power of Machine Learning in Healthcare: A Comprehensive Guide

By Sachi Nandan Mohanty, G. Nalinipriya, Om Prakash Jena, and Achyuth Sarkar

A Herculean Task: Presenting the Gamut of Machine Learning in Healthcare

With the rapid growth of machine learning in healthcare, it’s a daunting task to cover the entire spectrum of this field. Therefore, this book takes a unique approach, focusing on intensive and detailed computer simulations for all models presented, accompanied by explanations of the program code.

Distinguishing Features: Unique Chapters and Real-World Applications

This comprehensive guide includes distinct chapters on:

• Disease diagnosis and its applications
• Telemedicine and its role in healthcare
• Medical imaging and its advancements
• Smart health monitoring and its benefits
• Social media healthcare and its implications
• Machine learning for COVID-19: A paradigm shift

These chapters are designed to help readers develop a clear understanding of algorithms and strengthen their logical thinking.

Data Integration: A Dynamic Research Area in Healthcare

In the era of big data, answering a single question requires accessing multiple data sources and utilizing sophisticated analysis tools. This book delves into the world of data integration, a dynamic research area in the database community, and explores the development of middleware systems that provide seamless data access.

A Wide Audience: Students, Researchers, and Scientists

This book is intended for a broad audience, including students, researchers, and scientists from both academia and industry. With minimal prerequisites, readers are expected to have basic knowledge of machine learning. The comprehensive description of healthcare data management issues and an overview of existing systems make it an ideal resource for introductory and instructional purposes.

22 Innovative Chapters: Real-Time Applications and Expert Insights

This book is divided into 22 real-time innovative chapters, providing a variety of application examples in different domains. Written by the main inventors of the presented systems, each chapter specifies requirements and provides a description of both the chosen approach and its implementation. Due to the self-contained nature of these chapters, they can be read in any order.

Each chapter employs various technical terms, showcasing expertise in machine learning and computer science.

Product Details:

Publisher: Wiley-Scrivener

1st edition: April 13, 2021

Language: English

Hardcover: 416 pages

ISBN-10: 1119791812

ISBN-13: 978-1119791812