Identification of neurodegenerative diseases based on multivariate gate signal self-regression model and fusion in intelligent classifiers

Diagnosis of diseases with the help of new methods has received much attention. One of these diseases is amyotrophic lateral sclerosis. In this disease, neurons cause progressive and irreparable damage to the central nervous system (brain and spinal cord) and peripheral nervous system. Symptoms of upper motor neurons as well as symptoms of lower motor neurons are seen. It is possible to diagnose this disease from kinematic dynamics analysis data. Clinical methods in diagnosing this disease face significant errors. Machine learning methods are an effective way to diagnose these diseases. The proposed method of this research consists of five steps. Pre-processing, feature extraction, dimension reduction, classification and evaluation. The novelty of this article is in using classifier in the diagnosis of this disease. In classification fusion, the types of linear and non-linear classifications in a fusion method with each other will diagnose the disease with higher accuracy