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"This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine. It will combine the design and problem-solving skills of engineering with health sciences, in order to advance healthcare treatment. The book will include areas such as diagnosis, monitoring, and therapy. The book will provide real-world case studies, gives a detailed exploration of applications in healthcare systems, offers multiple perspectives on a variety of disciplines, while also letting the reader know how to avoid some of the consequences of old methods with data sharing. The book can be used as a reference for practitioners, researchers and for students at basic and intermediary levels in Computer Science, Electronics and Communications"--

Machine learning. --- Medical informatics.

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Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn Key Features Develop a range of healthcare analytics projects using real-world datasets Implement key machine learning algorithms using a range of libraries from the Python ecosystem Accomplish intermediate-to-complex tasks by building smart AI applications using neural network methodologies Book Description Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics. This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks. By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain. What you will learn Explore super imaging and natural language processing (NLP) to classify DNA sequencing Detect cancer based on the cell information provided to the SVM Apply supervised learning techniques to diagnose autism spectrum disorder (ASD) Implement a deep learning grid and deep neural networks for detecting diabetes Analyze data from blood pressure, heart rate, and cholesterol level tests using neural networks Use ML algorithms to detect autistic disorders Who this book is for Machine Learning for Healthcare Analytics Projects is for data scientists, machine learning engineers, and healthcare professionals who want to implement machi...

Medical informatics. --- Medicine --- Information technology.

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The book addresses privacy and security issues providing solutions through authentication and authorization mechanisms, blockchain, fog computing, machine learning algorithms, so that machine learning-enabled IoT devices can deliver information concealed in data for fast, computerized responses and enhanced decision-making. The main objective of this book is to motivate healthcare providers to use telemedicine facilities for monitoring patients in urban and rural areas and gather clinical data for further research. To this end, it provides an overview of the Internet of Healthcare Things (IoHT) and discusses one of the major threats posed by it, which is the data security and data privacy of health records. Another major threat is the combination of numerous devices and protocols, precision time, data overloading, etc. In the IoHT, multiple devices are connected and communicate through certain protocols. Therefore, the application of emerging technologies to mitigate these threats and provide secure data communication over the network is discussed. This book also discusses the integration of machine learning with the IoHT for analyzing huge amounts of data for predicting diseases more accurately. Case studies are also given to verify the concepts presented in the book

Internet of things. --- Medical informatics.

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Big data. --- Medical informatics. --- Soft computing.

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The United Nation's Sustainable Development Goals call for the establishment of Good Health and Well-being and target a universal digital healthcare ecosystem by 2030. However, existing technology infrastructure is ineffectual in achieving the envisioned target and requires massive reconfiguration to achieve its intended outcome. This book suggests a way forward with fair and efficient digital health networks that provide resource efficiencies and inclusive access to those who are currently under-served. Specifically, a fair and efficient digital health network that provides a common platform to its key stakeholders to facilitate sharing of information with a view to promote cooperation and maximise benefits. A promising platform for this critical application is ⁰́₈cloud technology⁰́₉ with its offer of computing as a utility and resource sharing. This is an area that has attracted much scholarly attention as it is well-suited to foster such a network and bring together diverse players who would otherwise remain fragmented and be unable to reap the benefits that accrue from cooperation. The fundamental premise is that the notion of value in a digital-health ecosystem is brought about by the sharing and exchange of digital information. However, notwithstanding the potential of information and communication technology to transform the healthcare industry for the better, there are several barriers to its adoption, the most significant one being misaligned incentives for some stakeholders. Thisbook suggests among other findings, that e-health in its true sense can become fair and efficient if and only if a regulatory body concerned assumes responsibility as the custodian of its citizens⁰́₉ health information so that ⁰́₈collaboration for value⁰́₉ will replace ⁰́₈competition for revenue⁰́₉ as the new axiom in delivering the public good of healthcare through digital networks.

Medical economics. --- Medical informatics --- Economic aspects.