Handbook Of Research On Disease Prediction Through Data Analytics And Machine Learning

By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

"The main goal of this book is to contribute to the development of new approaches and reliable enabling technologies in the healthcare industry that will enhance not only human quality of life, but also will lead to healthier, innovative, and secure societies as an all"--

This book is a comprehensive review of technologies and data in healthcare services. It features a compilation of 10 chapters that inform readers about the recent research and developments in this field. Each chapter focuses on a specific aspect of healthcare services, highlighting the potential impact of technology on enhancing practices and outcomes. The main features of the book include 1) referenced contributions from healthcare and data analytics experts, 2) a broad range of topics that cover healthcare services, and 3) demonstration of deep learning techniques for specific diseases. Key topics: - Federated learning in analysis of sensitive healthcare data while preserving privacy and security. - Artificial intelligence for 3-D bone image reconstruction. - Detection of disease severity and creating personalized treatment plans using machine learning and software tools - Case studies for disease detection methods for different disease and conditions, including dementia, asthma, eye diseases - Brain-computer interfaces - Data mining for standardized electronic health records - Data collection, management, and analysis in epidemiological research The book is a resource for learners and professionals in healthcare service training programs and health administration departments. Readership Learners and professionals in healthcare service training programs and health administration departments.

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

The complexity of cancer demands an integrated approach from both a cancer biology standpoint and a pharmaceutical basis to understand the different anticancer modalities. Current research has been focused on conventional and newer anticancer modalities, recent discoveries in cancer research, and also the advancements in cancer treatment. There is a current need for more research on the advances in cancer therapeutics that bridge the gap between basic research (pharmaceutical drug development processes, regulatory issues, and translational experimentation) and clinical application. Recent promising discoveries such as immunotherapies, promising therapies undergoing clinical trials, synthetic lethality, carbon beam radiation, and other exciting targeted therapies are being studied to improve and advance the studies of modern cancer treatment. The Handbook of Research on Advancements in Cancer Therapeutics serves as a comprehensive guide in modern cancer treatment by combining and merging the knowledge from both cancer biology and the pharmacology of anticancer modalities. The chapters come from multi-disciplinary backgrounds, including scientists and clinicians from both academia and various industries, to discuss nascent personalized therapies and big data-driven cancer treatment. While highlighting topic areas that include cancer prevention, cancer therapeutics, and cancer treatments through the lenses of technology, medicine/drugs, and alternate therapies, this book is ideally intended for oncologists, radiation oncologists, surgical oncologists, and cancer biologists, along with practitioners, stakeholders, researchers, academicians, and students who are interested in understanding the most fundamental aspects of cancer and the available therapeutic opportunities.

ICT has had a huge impact on businesses and organizations in general, with new business models, new marketing channels, and new markets being reached using these technologies. ICT can promote new strategies and enhancers to optimize various aspects of business, but this technology also provides important tools that can empower social entrepreneurship initiatives to develop, fund, and implement new and innovative solutions to social, cultural, and environmental problems. With the upheaval caused by the COVID-19 pandemic and its subsequent impact on the economy, the methods and tools used within this field will be forever impacted. ICTs and the digital economy are huge trends that will affect organizations in several dimensions, such as how to communicate and improve performance. Thus, new perspectives and research are needed to identify the trends emerging in these fields. The Handbook of Research on Entrepreneurship, Innovation, Sustainability, and ICTs in the Post-COVID-19 Era broadens the exploitation of entrepreneurship, innovation, and ICTs in a global approach to draw attention to multidisciplinary perspectives of these contexts and their influence in modern organizations. In addition, the book explores and discusses, through innovative studies, case studies, systematic literature reviews, and reports, the key developments in digital entrepreneurship, circular economy and digitalization, digital business models, digital market and internationalization, digital economy, trends and challenges for organizations, digital entrepreneurial ecosystems, IS/ICT in organizations, social aspects of information systems, and more. This book is ideally intended for business managers, industry professionals, entrepreneurs, practitioners, stakeholders, researchers, academicians, and students looking for how business and organizations are going to shift and advance in the post-COVID-19 era.

Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the internet of things in today’s modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

This volume takes the reader on a technological voyage of machine learning advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized several fields, including transportation, agriculture, finance, weather monitoring, and others. This book brings together researchers, authors, industrialists, and academicians to cover a vast selection of topics in ML, starting with the rudiments of machine learning approaches and going on to specific applications in healthcare and industrial automation. The book begins with an overview of the ethics, security and privacy issues, future directions, and challenges in machine learning as well as a systematic review of deep learning techniques and provides an understanding of building generative adversarial networks. Chapters explore predictive data analytics for health issues. The book also adds a macro dimension by highlighting the industrial applications of machine learning, such as in the steel industry, for urban information retrieval, in garbage detection, in measuring air pollution, for stock market predictions, for underwater fish detection, as a fake news predictor, and more.