Prediction of Parkinson’s Disease using Handwriting Analysis and Voice Dataset- A Review

Abstract

Parkinson's disease is a common neurological movement illness that impairs motor coordination. Parkinson’s disease (PD) symptoms and severity, however, differ from person to person. By extracting insights, trends, and possibilities from the data, data research can be utilized to uncover solutions to problems in medical research by utilizing data, machine learning algorithms, and cutting-edge technology. Among the less evident early signs of Parkinson's disease are tremors, muscle stiffness, imbalance problems, and difficulty walking. There is currently no test to detect the illness early on, when symptoms might not be evident. However, handwriting and hand- drawn subjects in humans have been linked to PD. In addition to being a useful tool for PD prediction, speech smearing functions as an early warning system. In order to control symptoms and maybe halt the disease's progression, early detection makes it possible to organize treatments and intervene promptly. For those with Parkinson's disease, early application of certain therapies and medications can extend survival and enhance quality of life.