Machine Learning for Neurodegenerative Disorders

Introduction

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders.

Focus Areas

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.

Neuroimaging 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.

Machine Learning Algorithms

Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders.

Neuroinformatics and Big Data

It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders.

Future Prospects and Ethical Considerations

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.

Intended Audience

This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.