Machine Learning for Neurodegenerative Disorders

Advancements and Applications

Sudip Paul, Biswajit Jena, Sanjay Saxena

• Publisher: CRC Press
• Year: 2025
• eBook ISBN: 9781040317921
• Topic: Biological Sciences
• Subtopic: Diagnostics Imaging
• Index: Biological Sciences

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

Table of contents

• Cover Page
• Half-Title Page
• Series Page
• Title Page
• Copyright Page
• Contents
• Preface
• About the Editors
• List of Contributors
• Chapter 1 Introduction to Machine Learning and Its Applications to Neuroscience
• Chapter 2 Machine Learning Techniques for Neuroimaging Analysis and Interpretation
• Chapter 3 An Empirical Study on Neurodegenerative Disorders: Natural Language Processing for Extracting Insights
• Chapter 4 AI-Driven Drug Discovery and Repurposing for Neurodegenerative Disorders
• Chapter 5 Machine Learning Methods for Predicting Freezing of Gait in Parkinson’s Disease Patients: Insights from Recent Clinical Trials
• Chapter 6 Leveraging Artificial Intelligence-based Deep Learning for Early Diagnosis of Alzheimer’s Disease: A Comparative Analysis of Neural Network Approaches
• Chapter 7 A Combination of Ensemble with MCDM Approach for the Prediction of Alzheimer’s Disease through Audio Data
• Chapter 8 An Enhanced Technique for Predicting Autism Spectrum Disorder Using Vote and AdaBoost Models
• Chapter 9 A Unique Machine Learning Approach to Intracranial Lesion Detection by Dual Segmentation for Anisotropically Diffused MRI Images
• Chapter 10 A Novel Machine Learning Model on EEG Signals-Based Driver’s Drowsiness Detection System
• Chapter 11 Challenges and Future Directions in Applying Machine Learning to Neurodegenerative Disorders
• Index