Machine Learning for Early Detection of Neurodegenerative Diseases

: Neurodegenerative disorders, encompassing conditions like Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS), pose formidable challenges to global healthcare. Timely identification and accurate prognosis are essential for effective intervention and improved patient outcomes. This study investigates the use of machine learning (ML) models specifically designed for early Parkinson's disease diagnosis and predictive modeling of ALS progression. In the realm of Parkinson's disease, a variety of ML techniques are applied to analyze diverse datasets, including clinical assessments, neuroimaging, and genetic information. Supervised learning algorithms, such as support vector machines and random forests, exhibit promising outcomes in distinguishing individuals with early-stage Parkinson's disease from healthy counterparts. Furthermore, deep learning models, particularly convolutional neural networks, demonstrate high accuracy in detecting subtle patterns within neuroimaging data associated with early Parkinson's disease pathology. Regarding Amyotrophic Lateral Sclerosis, predictive models play a pivotal role in estimating disease progression and identifying factors influencing its course. ML algorithms, including linear regression and recurrent neural networks, utilize longitudinal clinical data and biomarkers to predict the rate of ALS progression. These models contribute to tailoring treatment strategies and enable clinicians to optimize care plans for individuals grappling with this rapidly progressing neurodegenerative disorder.