Using Neural Networks and Feature Selection Algorithm to Perform Early Diagnosis of Alzheimer and Mild Cognitive Impairment Disease

Abstract

Alzheimer’s disease (AD) is now considered the most common type of dementia in the population. Although it is a degenerative and irreversible disease, if diagnosed early, medications may be administered to slow the progression of symptoms and provide a better quality of life for the patient. Herbert et al. and G`omez conducted studies with classifiers contained in the software Weka using a database with values of 120 blood proteins, and they noticed that they could classify the patient may or may not be diagnosed with AD with an accuracy rate of 93% and 65%, respectively. Thus, this study aims to use neural networks such as Multi-layer Perceptron, Extreme-learning Machine and Reservoir Computing to perform early diagnosis of a patient with or without AD and Mild Cognitive Impairment (MCI), another common type of disease. This article also envisions to utilize the Random Forest Algorithm to create a new signature with 3 protein. Through experiments it can be concluded that the best performance was obtained with the MLP and the signature with 3 proteins doesn’t have a great accuracy when compered of those available in the literature.