Preface

Overview

The novel application of artificial intelligence (AI) techniques, including machine learning, and deep learning analytics for design and forecasting models for disease management, is an emerging area in computer science, computation biology, information technology, bioinformatics, bioinformatics, and epidemiology. During the last 20 years, various AI approaches have been successfully applied in diverse fields. Medicine is one of the most active domains for AI techniques, since they facilitate model diagnostics information based on statistical data and reveal hidden dependencies between symptoms of major diseases.

The present capacity to develop, evaluate, manufacture, distribute, and administer effective medical countermeasures is inadequate to meet the burden of both recurrent and emerging outbreaks of infectious diseases. When such interventions are unavailable, public health measures and supportive clinical care remain the only feasible tools to slow an emerging outbreak. Decision-making under such circumstances can be greatly improved by the use of appropriate data and advanced analytics, such as infectious disease modeling. Furthermore, these analyses can guide decision-making when medical countermeasures become available, allowing them to be used more effectively.

Forecasting is an emerging analytical capability that has demonstrated value in recent outbreaks by informing policy and enabling real-time epidemic management decisions. Since healthcare is one of the key parameters in assessing the gross domestic product (GDP) of any country, it has become crucial to transition from traditional healthcare practices to a smart healthcare system. New healthcare technologies provide numerous opportunities to maximize disease recognition, analysis, and other natural variables that may affect it. Therefore, it is necessary to understand how computer-assisted technologies can best be utilized and adopted in the conversion to smart healthcare.

This book is an essential publication that widens the spectrum of computational methods that can aid in smart disease management. It contains several statistical and AI techniques that can be used to acquire data of many different diseases.

Objective

AI techniques for early disease detection and forecasting can reveal useful information to medical experts. As such, this volume intends to serve as a resource to elicit and elaborate on possible intelligent mechanisms for helping in the modeling, prediction, and diagnosis of the infected individuals, as well as providing means for detecting early signs of the disease. As such, this book examines numerous ML and DL techniques in the biomedical field that are used for detecting and forecasting disease management at the cellular level. It discusses applications for image segmentation, classification, neural networks, and a variety of computational intelligence approaches. It also discusses DL techniques for noise elimination and filtering of brain disease and hybrid ML techniques for diabetes prediction, mental illness, and stress level monitoring approaches.

We hope this book encourages an even wider adoption of these concepts and methods to assist physicians in problem solving and stimulates research that will lead to additional innovations in this area.

Organization

The volume comprises thirteen comprehensive chapters that encompass the modelling, prediction, and diagnosis of disease data. The arrangement of the chapters guides the reader from effective modelling of different disease data to detection and diagnosis. Chapter 1 presents an overview of how AI is transforming healthcare, as well as examples of consumer resistance to it, the economic implications of AI aversion, and ethical consideration and governance. Hand in hand with the emerging technology, healthcare systems are becoming progressively complex while constantly adapting to socioeconomic, epidemiological, and demographic changes, contributing to intricate global healthcare objectives.

Chapter 2 explores the use of integrated, and systematic moral cognitive therapy as a means of administering AI in the healthcare sector. The integration of AI in healthcare has the potential to improve prognosis with the help of an enormous database, but concerns have arisen regarding the vulnerability of a large section of end-users and the ethical implications of its use.

Social inequities, poverty lack of education, and risk of lifestyle diseases are mutually complementary to each other. An unhealthy lifestyle claims maximum responsibility among the factors of becoming a victim of lifestyle diseases. Zero preventive options other than lifestyle modification have enhanced the complication of management of such mass health issues.

Chapter 3 investigates a conceptual model that can bridge the gaps between healthcare, policy, and disease prevention. Such a model can assist with the recording and reporting of data to analyze the victim's pattern of lifestyle adoption in place of the current healthcare alternative. Color Filter Array (CFA) data can be efficiently compressed using existing image compression algorithms such as JPEG. In addition, it tested the overall performance of CFA compression methods and demosaicing algorithm selection. These CFA compression methods exploit how CFA images do not contain repetitive data introduced by demosaicing.

Chapter 4 establishes a relationship between the discovery of different CFA compression techniques and describes how the choice of demosaicing algorithm affects visual quality. Using a simple bilinear demosaicing method, CFA image compression produces better images compared to the traditional compression scheme.

Through image processing techniques, digital images can be compressed, monitored, and transmitted effectively in remote settings. Digital images have a significant effect on modern society, science, technology, and art. Some techniques utilized in image enhancement include filtering, segmentation, object recognition, and image fusion. Image processing techniques have played a crucial role in advancing healthcare, particularly in the field of smart healthcare. Chapter 5 reviews image processing for medical applications using secure smart healthcare techniques. Furthermore, the various challenges that scientists must resolve because of disease are also discussed. Image enhancement techniques engage an essential and fundamental task and interpretability of biomedical images, enabling accurate diagnosis and analysis. This chapter provides a comprehensive overview of image enhancement techniques that are specifically tailored for biomedical images. Chapter 6 presents various image enhancement methods designed specifically for biomedical imaging. The chapter evaluates and contrasts the usefulness of various approaches and the extent to which they are beneficial to the biomedical field.

Disease progression prediction is essential for patient care and therapeutic development. Clinical, genetic, and molecular data related to Parkinson's disease (PD) are collectively gathered by the Accelerating Medicines Partnership (AMP). Chapter 7 presents parameters linked to Parkinson's disease progression and evaluates the appropriateness of the AMP PD dataset for predictive modeling through an Exploratory Data Analysis (EDA). The onset of the Covid-19 pandemic and the practice of social isolation have led to a rise in instances of mental health issues. Advances in machine learning in the present decade have created additional opportunities for detection and the identification of mental health issues. Chapter 8 investigates the automatic prediction and diagnosis of mental illness using machine learning algorithms.

One of the most common neurodegenerative illnesses that affect older adults and mostly impair memory in the brain is Alzheimer's disease. Chapter 9 presents three models, efficient net B2, efficient net B3, and efficient net B4 architecture-based deep learning solutions for detecting and categorizing Alzheimer's disease.

One of the biggest issues in today's world is mental stress. Now that age is not taken into account when calculating stress levels, it makes no difference. Depression, heart attacks, and even suicide can result from extreme stress. Stress may have an impact on many facets of our existence, such as our behavior, emotions, and capacity for thought. Chapter 10 presents a machine-learning approach for measuring a person's stress level using three crucial parameters: body temperature, step count, and humidity.

Diabetes Mellitus is commonly known as a metabolic issue in which the body cannot utilize insulin, store glucose for energy, or produce insulin. Diabetes patients can suffer various sicknesses that incorporate kidney disappointment, stroke, visual impairment, cardiovascular failures, and lower appendage removal. Chapter 11 presents a hybrid-based machine learning approach for identifying the potential chances of contracting diabetes diseases.

The increasing adoption of smart healthcare care and Internet of Things (IoT) devices has revolutionized the healthcare industries, offering enhanced patient monitoring, diagnosis, and treatment. However, the integration of these devices into the healthcare ecosystem raises serious concerns regarding the security and privacy of sensitive medical records. Chapter 12 contributes to strengthening the overall cyber security framework within the healthcare industry, ensuring patients' trust in smart healthcare IoT devices and fostering the continued growth of remote patient monitoring.

The proliferation...