Advanced Machine Learning and Deep Learning Architectures for Enhanced Early Diagnosis and Risk Stratification of Neurodegenerative Diseases in Precision Healthcare

Abstract

Neurodegenerative diseases (NDs) such as Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease represent a growing global health burden characterized by progressive neuronal loss and irreversible cognitive and motor decline. Early diagnosis and accurate risk stratification remain critical challenges due to the heterogeneous and multi-factorial nature of these disorders. Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) architectures, have demonstrated transformative potential in addressing these challenges through multimodal data integration, predictive modeling, and automated clinical decision support. This review critically synthesizes state-of-the-art ML and DL frameworks applied in precision healthcare for neurodegenerative disease detection and progression modeling. It explores the role of feature-based learning, imaging genomics, digital biomarkers, and biosensor-driven diagnostic systems in enhancing diagnostic accuracy. Furthermore, the study evaluates interpretability challenges, ethical considerations, and clinical integration barriers associated with AI deployment in healthcare systems. Evidence suggests that hybrid architectures combining convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models significantly improve early-stage detection performance and risk classification accuracy. However, limitations persist in dataset heterogeneity, model generalizability, and clinical validation. The findings emphasize the need for standardized AI frameworks and interdisciplinary collaboration to enable scalable precision diagnostics. Overall, this study highlights AI-driven architectures as a foundational pillar in next-generation neurodegenerative disease management systems.