NEUROPREDICT AI: UTILIZING MACHINE LEARNING FOR EARLY DIAGNOSIS OF NEURODEGENERATIVE CONDITIONS

Neurodegenerative diseases like Alzheimer’s, Parkinson’s and Huntington’s progressively impair brain function,
leading to cognitive and motor decline. Early detection is vital it enables timely intervention, personalized care,
and improved outcomes. Yet, traditional diagnostics rely on late-stage symptoms, delaying effective treatment.
However, conventional diagnostic methods rely on symptomatic evaluation, which often leads to late-stage
detection.
The purpose of AI-driven early detection systems is to identify neurodegenerative diseases at an earlier stage by
leveraging machine learning and advanced neuroimaging techniques. These systems analyse medical imaging,
genetic markers, speech patterns, and motor functions to detect subtle biomarkers indicative of disease onset. By
improving diagnostic accuracy and enabling proactive intervention, AI-based methods have the potential to
revolutionize neurological healthcare and research. This paper explores the role of AI in early detection, its
benefits, limitations and future prospects in transforming neurodegenerative disease diagnosis.