Detection of Parkinson’s Disease using Deep learning algorithms

Abstract

Parkinson’s illness is an advancing genetic neurological chronic disease impacts people mostly in old age but still might infect very few young people. This disease slowly eats up a part of the brain which is responsible for body movement, resulting in a steady loss of muscle control of the entire body. For example, frequent hand and leg tremors, body stiffness, loss of speech, bradykinesia, and dystonia. The treatments available don’t entirely cure PD as there is no medication, but on the other side, clinicians are trying to improve the patient’s lifetime. As the pattern recognition region of the brain is related to PD, we are using a dataset with healthy and PD hand-drawn images from a small test conducted. Here we have proposed a combination of deep learning algorithms of ANN and CNN with a machine learning algorithm of Random Forest classifier to improve the accuracy rate by “74” in finding out the person with PD. Hence, it is inferred that the expected results benefit clinicians in identifying and treating patients with PD in an operative way.