Preface

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.

The book is a companion and a must-read, for academicians, people from industries, graduate and post-graduate students, researchers, physicians and for everyone who is involved in the fields of medicine, data analytics or machine learning directly or indirectly. The book is compiled in such a way that each chapter is sufficient to give a complete study set from problem formulation to its solutions. All chapters are independent of each other and can be studied individually without consulting other chapters.

Each chapter starts with an abstract, important key terms, and an introduction to the topic. It is followed by related works, challenges identified, methodology, and experimental results. The chapter ends with the concluding remarks and future directions.

This book includes chapters in the following research areas:

• Review in the fields of Data Analytics, Machine Learning, and Medical Data Analysis.
• Federated Learning in Healthcare.
• 3-Dimensional Image Reconstruction.
• Applications and Practical Systems for Healthcare.
• Information Retrieval from medical data.
• Data Integration.
• Prediction Models.
• Clinical Decision Support Systems.
• Computer-Aided Diagnosis.
• Mobile Imaging for Biomedical Applications.

A brief summary of book chapters is given below:

Chapter 1: Role of Federated Learning in Healthcare: A Review

In this chapter, the authors provide a detailed comparative study of the different deep learning-based models employed in federated learning. They discussed how efficiently the model can classify chest radiographs into Covid-19, pneumonia, and normal categories. This chapter provides the benchmarking information and analysis for the researchers looking forward to developing deep learning-based applications of federated learning in healthcare.

Chapter 2: Role of Artificial Intelligence in 3-D Bone Image Reconstruction: A Review

This chapter presents a review of the bone imaging techniques and techniques applied for the conversion of two-dimensional images into three-dimensional form. It also gives directions for developing patient-specific and organ-specific optimized techniques for 3-D reconstruction.

Chapter 3: Role of Machine Learning and Deep Learning Techniques in Detection of Disease Severity: A Survey

This chapter explores the role of machine learning and deep learning techniques in the detection of disease severity. It presents a survey of the latest methodologies and algorithms employed in analyzing medical data to predict and assess the severity of various diseases, empowering clinicians with valuable insights for personalized treatment plans. The chapter highlights the advantages and drawbacks of different ML and DL techniques employed for prediction of disease severity.

Chapter 4: Computer-Aided Bio-Medical Tools for Disease Identification

This chapter investigates computer-aided biomedical tools for disease identification. It discusses the development and utilization of innovative software tools and techniques that assist in the identification and diagnosis of diseases, augmenting healthcare professionals' decision-making process. The chapter highlights the importance of computer-aided bio-medical tools as techno-assistants for health experts.

Chapter 5: Prognosis of Dementia using Machine Learning

In this chapter, the authors discuss the prognosis of dementia using machine learning. They explore the potential of machine learning algorithms in predicting the progression and prognosis of dementia, offering valuable insights for early interventions and personalized care plans.

Chapter 6: A Clinical Decision Support System for Effective Identification of Onset of Asthma Disease

This chapter presents a clinical decision support system for the identification of asthmatics in two different cohorts representing rural and urban populations in India. It provides details about developing a hybrid decision support system by uniquely combining unsupervised and supervised learning techniques.

Chapter 7: Applying Deep Learning and Computer Vision for Early Diagnosis of Eye Diseases

This chapter presents a study to raise awareness about various eye disorders. It provides a discussion on the role of computer vision, image processing, and deep learning techniques in the early diagnosis of the disease. Thus, it may prove useful for enhancing early disease treatment and minimizing the chances of blindness.

Chapter 8: The Fusion of Human-Computer Interaction and Artificial Intelligence Leads to the Emergence of Brain Computer Interaction

In this chapter, the authors discuss the Components Brain Computer Interface, its characteristics and challenges. They provide details of how conventional classifiers are replaced with Convolutional Neural Networks (CNNs). The chapter also reveals that the EEG signals from the brain can be linked seamlessly to mechanical systems via BCI applications, making it a rapidly growing technology. The presented technology has applications in the fields such as Artificial Intelligence and Computational Intelligence.

Chapter 9: Mining Standardized EHR Data: Exploration, Issues, and Solution

This chapter focuses on mining standardized Electronic Health Records (EHR) data, providing an in-depth exploration. This chapter examines the process of extracting knowledge and insights from standardized EHR data through data mining techniques. It explores the challenges and opportunities associated with mining EHR data, including data quality issues, data integration challenges, and ethical considerations of handling sensitive patient information. Additionally, the chapter presents innovative solutions and methodologies for effectively mining EHR data to support various healthcare applications such as clinical decision-making, predictive analytics, and population health management.

Chapter 10: Role of Database in Epidemiological Situation

This chapter presents the crucial role of databases in epidemiological situations. It highlights the significance of databases in epidemiological research, providing a comprehensive overview of their role in data collection, management, and analysis. In this chapter, the authors explore different types of databases commonly used in epidemiology, including disease surveillance systems. Moreover, the chapter discusses the challenges and considerations associated with database implementation, such as data standardization, and privacy protection.