Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Normal Based on Neuropsychological Data via Supervised Learning

Dementia is neurodegenerative or vascular disorder which is characterized by declining mental function including a combination of symptoms for abnormal activity, behavior and cognitive. Alzheimer's disease (AD) is the most common type of dementia, it accounts for 60 to 80 percent of dementia cases. Their brain neuron are die and loss the connection with each other neurons which stop firing and establishing the brain network, and it is easily considered as normal aging processes when the subject is actually in the early stages of AD. This paper proposed a machine learning algorithm based on neuropsychological data to classify subjects into Alzheimer's disease, mild cognitive impairment (MCI) and cognitively normal (CN). We acquired neuropsychological data from 678 participants with clinical diagnosis information from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and we focus on Mini-Mental State Examination (MMSE) which is the most extensively used psychometric examination in the clinical practice. Through computer-aided diagnosis (CAD) system which is called as 'smart doctor', this algorithm can help doctors diagnose patients with Alzheimer's disease. The medical AI brings accurate and rapid diagnosis and prediction. As to follow up the patient's care and social resources can be given faster. The main result of this paper is that we found two valuable features from MMSE, orientation and recall, which have the same ability as the entire MMSE, to detect the Alzheimer's disease.