Prediction of Alzheimer’s Dementia Disease Stages and Cognitive Scores Using Machine Learning and Deep Learning

Abstract

Abstract: The most frequent form of dementia which is characterized by a low deterioration of memory, thinking, actions, and social skills, is Alzheimer's disease (AD). Those alterations have a ripple effect on an individual's ability to fulfil their duties. According to present thinking, the erroneous protein build-up in and around brain cells present thinking, the erroneous protein build-up in and around brain cells ultimately causes Alzheimer's disease. AD is not restricted to people over 65; it also affects individuals of all ages. The brain diminishes as the outcome of Alzheimer's disease, and subsequently, brain cells die away. In the early stages of the illness, victims exhibit modest memory loss, which is followed by a decrease in their capability to verbally interact and converse. While there is no curative treatment for the disease, early identification could decrease the severity of the illness and permit patients to lead quality lives. Alzheimer's disease, which particularly impacts elderly individuals and advances neurodegenerative, is a significant factor that leads to dementia. The disease can be divided into 5 stages: Cognitively Normal (CN), Mild Cognitive Impairment (MCI), Early MCI (EMCI), Late MCI (LMCI), and Alzheimer’s Disease (AD). These stages are predicted using a well-developed AlexNet model and an OASIS dataset is utilized to predict Cognitive Scores based on the Mini-Mental State Examination (MMSE) using different machine learning algorithms.