Making the Discrimination in the Walking Parameters of Individuals with Multiple Sclerosis and Parkinson's Disease with Machine Learning

Abstract

Objective: To determine the contribution of gait analysis to the differentiation and diagnosis of these diseases by examining the walking videos of individuals diagnosed with multiple sclerosis (MS) and Parkinson's disease (PD) using the deep learning method. Materials and Methods: A hybrid system based on Convolutional Neural Networks was developed for the detection of MS and PD based on gait analysis. The patients were walked on a flat surface of approximately 14 meters and video recordings were taken from the front, back and both sides during walking. Videos of a total of 28 patients, 12 PD and 16 MS patients, were used in the study. Results: In the study, the data was analyzed using machine learning techniques and the best accuracy score was obtained as 87.5%. Conclusion: The accuracy rate of machine learning models in the diagnosis, follow-up and treatment process of patients such as MS, PD and other neurological diseases has been examined and it has been concluded that it is inevitable that these methods will be used much more over time.