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Preface

The use of computing technologies in the healthcare domain has been creating new avenues for facilitating the work of healthcare professionals. Several computing technologies, such as machine learning and virtual reality, have been flourishing and in turn creating new possibilities. Computing algorithms, methodologies and approaches are being used to provide accurate, stable and prompt results. Moreover, deep learning, an advanced learning technique, is striving to enable computing models to mimic the behavior of the human brain; and the Internet-of-Things (IoT), the computer network consisting of “things” or physical objects in addition to sensors, software or methods, is connecting to and exchanging data with other devices. Therefore, the primary focus of this book, Emerging Technologies for Healthcare, is to discuss the use and applications of these IoT and deep learning approaches for providing automated healthcare solutions.

Our motivation behind writing this book was to provide insight gained by analyzing data and information, and in the end provide feasible solutions through various machine learning approaches and apply them to disease analysis and prediction. An example of this is employing a three-dimensional matrix approach for treating chronic kidney disease, the diagnosis and prognostication of acquired demyelinating syndrome (ADS) and autism spectrum disorder, and the detection of pneumonia. In addition to this, providing healthcare solutions for post COVID-19 outbreaks through various suitable approaches is also highlighted. Furthermore, a detailed detection mechanism is discussed which is used to come up with solutions for predicting personality through handwriting recognition; and novel approaches for sentiment analysis are also discussed with sufficient data and its dimensions.

This book not only covers theoretical approaches and algorithms, but also contains the sequence of steps used to analyze problems with data, processes, reports, and optimization techniques. It will serve as a single source for solving various problems via machine learning algorithms. In brief, this book starts with an IoT-based solution for the automated healthcare sector and extends to providing solutions with advanced deep learning techniques.

Here, we would like to take the opportunity to acknowledge the assistance and contributions of all those engaged in this project. We especially would like to thank our authors for contributing their valuable work, without which it would have been impossible to complete this book. We express our special and most sincere thanks to the reviewers involved in the review process who contributed their time and expertise to improving the quality, consistency, and arrangement of the chapters. We also would like to take the opportunity to express our thanks to the team at Scrivener Publishing for giving the book its final shape and introducing it to the public.

Editors Monika Mangla, Nonita Sharma, Poonam Mittal, Vaishali Mehta Wadhwa, Thirunavukkarasu K. and Shahnawaz Khan