Machine Learning for Neurodegenerative Disorders

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.