Signals Before Symptoms: MACHINE LEARNING FOR Early Detection " Symptoms" of Parkinson's and Alzheimer's Disease from Imaging, Speech, Gait, Clinical Data

What if the earliest signs of Parkinson's and Alzheimer's disease are already hidden in the data and machine learning can reveal them before symptoms appear?Signals Before Symptoms is a practical, rigorous guide to building trustworthy machine-learning systems for early-detection research. Written for researchers, engineers, clinicians, and postgraduate students, it explores meaningful signals across MRI, PET, DaTSCAN, speech, gait, wearables, handwriting, digital biomarkers, and clinical, cognitive, and genetic data.Move beyond inflated accuracy. Learn to prevent data leakage, harmonise multisite datasets, manage missing modalities, evaluate performance at real-world prevalence, test explanations, and communicate uncertainty honestly. Twelve revealing case files expose why promising models fail, while a complete worked study demonstrates the journey from research question to defensible conclusion.Featuring exercises, reporting checklists, dataset guidance, code recipes, and global perspectives on regulation, ethics, deployment, and equity, this book helps you create research that is reproducible, clinically meaningful, and built to withstand scrutiny.