Survey of Parkinson's Disease Detection using Different Symptoms

Parkinson's Disease (PD) is an acute ailment, that occurs as a result of the loss of cells in the substantia nigra of the brain that makes dopamine. It has a huge negative impact on a human's quality of life. People affected with PD have trouble in speaking, writing, and walking. Brain is the main part that will be affected first, in persons with PD. It can be diagnosed with several motor symptoms like tremor, rigidity, slow movement and postural instability. Studies revealed that 90% of people with PD have issues with their speaking. As the disease impact grows, the patient's tone becomes highly distorted. Speech analysis has been used drastically, in order to construct the telemonitoring and tele diagnosing models for prediction. The most important goal of this research is to look at the survey work done considering different symptoms, to diagnose PD. Many machine learning and deep learning algorithms are being employed till date and as a result, Deep learning algorithms resulted with the best accuracy of 99.34% and Machine learning algorithms resulted with an accuracy of 97.1%, when scanned brain images are considered for analysis, to classify PD. Developing a better detection system to identify PD at the early stages, is highly demanding. Artificial intelligence is serving as a great learning tool that is adding value to problem-solving situations, particularly in the field of medical diagnosis.