This review paper delves into the transformative impact of machine learning on the diagnosis of neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, and ALS. The importance of high-quality medical data, diverse data sources, and specific machine learning algorithms, including Support Vector Machines, Convolutional Neural Networks, and Long Short-Term Memory networks, is emphasized. Case studies showcase the practical applications of machine learning, highlighting the methodologies, findings, and limitations of various projects. Machine learning’s significance in advancing
neurodegenerative disease diagnosis lies in its ability to enable early detection, enhance diagnostic accuracy, and facilitate personalized treatment strategies. The future of machine learning in this field is marked by the integration of diverse data modalities, interpretable models, ethical considerations, the recognition of disease heterogeneity, and the pursuit of early detection and intervention. Collaboration among researchers, clinicians, and technologists is pivotal for realizing the potential of machine learning in improving patient care and quality of life for individuals affected by these challenging
diseases. As we venture into the future, machine learning promises to continue pushing the boundaries of what is achievable in neurodegenerative disease diagnosis.