Diagnosis of Alzheimer’s Disease using Machine Learning Algorithms

Abstract

Abstract: Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and is considered to be the main cause of cognitive impairment in elderly people. It is a progressive disease that destroys memory and other important mental functions and causes problems with memory, thinking and behavior. Symptoms usually develop slowly and worsen over time Symptoms may become severe enough to interfere with daily life, and lead to death. In 2022, 55 million people worldwide suffered from this disease. AD is predicted to affect 1 in 85 people globally by 2050, and at least 43% of prevalent cases need a high level of care. Alzheimer's Disease Neuroimaging Initiative (ADNI) give datasets that can be utilized for different Alzheimer's Disease related examinations. The dataset consists of a longitudinal MRI data of 150 subjects aged 60 to 96.72 of the subjects were grouped as 'Nondemented' throughout the study.64 of the subjects were grouped as 'Demented' at the time of their initial visits and remained so throughout the study.14 subjects were grouped as 'Nondemented' at the time of their initial visit and were subsequently characterized as 'Demented' at a later visit. These fall under the 'Converted' category. In our project, we propose some machine learning models to detect the Alzheimer's disease in earlier stage by finding the accuracy levels and determining the attributes that helps us to find the maximum accuracy rate.