Classification of Alzheimer’s Disease Mild Cognitive Impairment and Normal Aging based on Neuropsychological Data via Multilayer Perceptron and Support Vector Machine

Abstract

Dementia is a general term for a decline in mental ability which is severe enough to interfere with patient’s daily life Alzheimer's disease (AD) is the most common type of dementia it accounts for 60 to 80 percent of dementia cases Because AD is one kind of neurodegenerative diseases it is easily considered as normal aging processes when the subject is actually in the early stages of AD and different doctors may have different opinions on the status of the same subject This thesis proposed a machine learning algorithm based on neuropsychological data to classify subjects into Alzheimer’s disease mild cognitive impairment and normal aging Through the automatic computer-aided diagnosis doctors may have diagnostic results to refer to before they make any decisions We acquired neuropsychological data from 399 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 The major contribution of this thesis 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 The goal is to benefit doctors and patients with faster detection of the disease which can in reverse to have the medical resources being used more efficiently

Date of Award	2018 May 2
Original language	English
Supervisor	Gwo-Giun Lee (Supervisor)