Alzheimer's disease is one of the most common neurodegenerative disorders that predominantly affect memory. This happens when neurons lose their structure and function over time. Since there is currently no cure for Alzheimer's disease, it is important to identify the disease early and to slow its development as much as possible. Various computational approaches have been used in various studies to diagnose Alzheimer's disease. The main purpose of this paper is to analyze feature extraction and classification algorithms in order to determine the best method for diagnosing Alzheimer's disease. The following sections make up this paper: (i) a brief overview of the disease and the case; and (ii) a study of feature extraction and classification algorithms