Parkinson’s Disease Prediction Using Deep Learning Classification Algorithms

Abstract

Abstract: complaints arising from neurological disorders continue to increase today. At the same time, studies on diagnosis and treatment methods in medicine are increasing as technology advances. With the increasing interest in these areas, studies have been carried out on various diagnosis and follow-up systems related to Parkinson’s disease. For this purpose, in this study, we studied the classification of a data set consisting of various voice recordings for each patient with the designed deep learning architecture in order to assist in the more objective diagnosis of Parkinson’s disease. Although it is important for the estimation of the study to find different sound samples of each subject in the data set, it is not known how much these recordings represent all the sound recordings of the person. Recurrent neural networks, which are a deep learning architecture, are an efficient system that can achieve high success in voice data and can be preferred in the diagnosis and follow-up of Parkinson’s disease. However, this study showed that in such a network design, much larger and more diverse data are needed to increase the classification rate, to make more accurate predictions in the field of medicine, and to make remote diagnosis.