Experimental Results of CNN and RNN Models for Identification of Parkinson’s Disease

There are many people with neurological disorders, and these disorders lead to dysarthria. Dysarthria, makes it more difficult for croakers to control the issue if not addressed promptly. The psyche and physiology of the patient will be affected by the symptoms. The majority of early studies on diagnosis dysarthria employed deep or machine learning methods to categorize these symptoms. In order to describe about dysarthria, this study suggests a CNN-LSTM model using convolutional neural networks and reopened intermittent units. According to experimental researches the CNN-LSTM model performs better in detecting dysarthria than the previous models. The study's suggested model has a delicacy of 98.38. Compared to past exploration models, this is more precise.