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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
In the recent era, the use of data analytics and Machine Learning algorithms has been observed in the arena of the medical field. Literature shows the successful application of data analytics and Machine Learning techniques for making predictions using real-time data collected from medical fields. The efficacy of machine learning models in image processing, big data analytics, object detection, automatic extraction, and tailoring of features is a great motivation for employing these models in the medical field. A boom in the use of Machine Learning and Deep Learning models is observed since the last decade. These models automatically extract the features from medical images, identify the most prominent features and predict diseases such as pneumonia, COVID-19, emphysema, lung tuberculosis, tumor, etc. can be predicted by training the Deep Learning model with chest radiographs and CT scans. These models not only predict the disease but are also useful in visualizing the infection in the organs. For reliable prediction, there is a need to design the custom architecture of the model. The architecture designer must focus on the size of the dataset, versatility, and quality of the dataset, types and number of predictions to be provided. The architecture is also dependent on the type of analysis required for disease prediction.
Literature reveals a lot of information about the design of methods for disease prediction. But, poor availability of systematic information at one source becomes challenging for the students, academicians as well as researchers working in this field. Researchers face problems in identifying suitable algorithms for pre-processing, transformations, and integration of clinical data. They also seek different ways to build models, and prepare data sets for training and evaluating the models. Moreover, it becomes significant for them, to observe the impact of decision-making strategies on the accuracy and precision of the predictive models designed on the basis of techniques such as Logistic Regression, Neural Networks, Decision Trees, and Nearest Neighbors. Thus, there is a strong need of providing well-organized study material with practical aspects and validation. The book smartly fills the gaps.
This book invited ideas, proposals, review articles and experimental works from the researchers working in the field. The systematic organization of the research works in the field of applying Machine Learning for disease prediction will be fruitful in providing insights to readers about the existing works and the gaps available in the field. This book is a significant contribution towards providing a detailed study of data analytics algorithms and Machine Learning techniques for disease prediction. The book includes a rigorous review of related literature, methodology for data set preparation, model building, training, and testing the model. It contains a comparative analysis of versatile algorithms applied for making predictions in the challenging arena of medical science and disease prediction. The provides good insight into the topics such as Data Analytics, Machine Learning, Deep Learning, Information Retrieval from medical data, Data Integration, Prediction Models, Medical Data Analysis, Medical Decision Support systems, Federated Learning in Healthcare, and Medical Image Reconstruction.
PREFACE
CHAPTER 1 ROLE OF FEDERATED LEARNING IN HEALTHCARE: A REVIEW
CHAPTER 2 ROLE OF ARTIFICIAL INTELLIGENCE IN 3-D BONE IMAGE RECONSTRUCTION: A REVIEW
CHAPTER 3 ROLE OF MACHINE LEARNING AND DEEP LEARNING TECHNIQUES IN DETECTION OF DISEASE SEVERITY: A SURVEY
CHAPTER 4 COMPUTER-AIDED BIO-MEDICAL TOOLS FOR DISEASE IDENTIFICATION
CHAPTER 5 PROGNOSIS OF DEMENTIA USING MACHINE LEARNING
CHAPTER 6 A CLINICAL DECISION SUPPORT SYSTEM FOR EFFECTIVE IDENTIFICATION OF THE ONSET OF ASTHMA DISEASE
CHAPTER 7 APPLYING DEEP LEARNING AND COMPUTER VISION FOR EARLY DIAGNOSIS OF EYE DISEASES
CHAPTER 8 THE FUSION OF HUMAN-COMPUTER INTERACTION AND ARTIFICIAL INTELLIGENCE LEADS TO THE EMERGENCE OF BRAIN COMPUTER INTERACTION
CHAPTER 9 MINING STANDARDIZED EHR DATA: EXPLORATION, ISSUES, AND SOLUTION
CHAPTER 10 ROLE OF DATABASE IN EPIDEMIOLOGICAL SITUATION