Different Machine Learning Approch's for Diagnosis of Alzheimer's Disease and Vascular Dementia

Abstract

The two frequent neurodegenerative diseases are Alzheimer's disease (AD) and vascular dementia (VD). When employing traditional clinical and MRI criteria, AD, and VD can share a number of neurological problems, which might lead to a contested diagnosis. Various strategies are required to overcome this challenge. It has been established that the clinical accuracy of various neurodegenerative illnesses, include dementia, can be enhanced by integrating magnetic resonance imaging (MRI) and machine learning (ML). To that end, this study looked at two questions: first, whether various ML algorithms combined with cutting-edge MRI features can help distinguish VD from AD, and secondly, if the created method can forecast the frequent disease in people with an ambiguous characteristic of AD or VD. ‘Random Forest’ and ‘K-Nearest Neighbor’ are the two machine learning algorithms used to distinguish between AD and VD.