LAUSR

Alzheimer’s disease (AD) is non-repairable brain disorder which impacts a person’s thinking along with shrinking the size of the brain, ultimately resulting in the death of the patient. It is necessary for the treatment of initial stages in AD so that the further degeneration could be delayed. This diagnosis can be achieved with the application of machine learning techniques which employ various optimization and probabilistic techniques. Hence with an objective of distinguishing people with normal brain ageing from those who would develop Alzheimer’s disease, this paper presents an effective machine learning model that successfully diagnosed AD, cMCI, ncMCI and CN which are being detected during pre-stages by itself.