Authors: Priya Deshmukh
Abstract: Neurodegenerative diseases (NDs), such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS), impose a significant burden on public health worldwide. These diseases typically develop insidiously over years, with symptoms becoming apparent only after substantial neuronal loss has occurred. Early and accurate diagnosis is paramount to implementing interventions that could delay progression, improve patient quality of life, and optimize healthcare resources. In recent years, machine learning (ML) has emerged as a revolutionary approach for processing complex biomedical data to assist in early diagnosis and prognosis of neurodegenerative conditions. This paper comprehensively explores the diverse machine learning methodologies applied to early ND diagnosis, emphasizing the role of neuroimaging, molecular biomarkers, genetic data, and clinical assessments. It discusses the entire diagnostic pipeline from data acquisition to model deployment, addresses challenges such as data heterogeneity and interpretability, and outlines future directions to integrate ML-based systems into clinical practice effectively.
DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.554
