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Design and Forecasting Models for Disease Management 1st Edition

The book provides an essential overview of AI techniques in disease management and how these computational methods can lead to further innovations in healthcare.

Design and Forecasting Models for Disease Management is a resourceful volume of 13 chapters that elaborates on computational methods and how AI techniques can aid in smart disease management. It contains several statistical and AI techniques that can be used to acquire data on many different diseases. The main objective of this book is to demonstrate how AI techniques work for early disease detection and forecasting useful information for medical experts. As such, this volume intends to serve as a resource to elicit and elaborate on possible intelligent mechanisms for helping detect early signs of diseases. Additionally, the book examines numerous machine learning and data analysis techniques in the biomedical field that are used for detecting and forecasting disease management at the cellular level. It discusses various applications of image segmentation, data analysis techniques, and hybrid machine learning techniques for illnesses, and encompasses modeling, prediction, and diagnosis of disease data.

Contents

۱ A Study of Possible AI Aversion in Healthcare Consumers
۲ A Study of AI Application Through Integrated and Systematic Moral Cognitive Therapy in the Healthcare Sector
۳ A Strategic Model to Control Non-Communicable Diseases
۴ Image Compression Technique Using Color Filter Array (CFA) for Disease Diagnosis and Treatment
۵ Research in Image Processing for Medical Applications Using the Secure Smart Healthcare Technique
۶ Comparative Study on Image Enhancement Techniques for Biomedical Images
۷ Exploring Parkinson’s Disease Progression and Patient Variability: Insights from Clinical and Molecular Data Analysis
۸ A Survey-Based Comparative Study on Machine Learning Techniques for Early Detection of Mental Illness
۹ Diagnostics and Classification of Alzheimer’s Diseases Using Improved Deep Learning Architectures
۱۰ Perform a Comparative Study Based on Conventional Machine Learning Approaches for Human Stress Level Detection
۱۱ Diabetes Prediction Using a Hybrid PCA-Based Feature Selection and Computational Machine Learning Algorithm
۱۲ A Robust IoT-Based Approach to Enhance Cybersecurity and Patient Trust in the Smart Health Care System: Zero-Trust Model
۱۳ Safeguarding Digital Health: A Novel Approach to Malicious Device Detection in Smart Healthcare