Parkinson’s Disease Detection using Machine Learning Algorithm

Parkinson's disease (PD) is a neurological ailment characterised by the gradual onset of motor symptoms such as tremors and muscle stiffness. It might be challenging to make a correct diagnosis of PD in its early stages since the symptoms may be similar to those of other conditions or to age-related physiological changes. This research uses convolutional neural networks, a type of deep neural network, to aid in the diagnosis of Parkinson's disease. With the help of the patient, the drawing exercises are employed to detect abnormal motor patterns. When trained on pictures of spiral drawings, the proposed convolutional classifier successfully distinguishes PD patients from controls 91.67% of the time. An improved version of the proposed program that can do a specific diagnostic procedure automatically and with minimum human interaction is not far off.