Eunice Daphne J, Gayathri J, Deepa B B
Parkinson’s disease is a degenerative disease of the central nervous system caused by progressive degeneration of dopamine containing cells (neurons) which gradually causes the patients to have difficulty in walking, talking or completing other simple tasks. There have been many contributions in the field of detecting, monitoring and diagnosis of Parkinson’s disease. Speech processing and neural networks have been widely in the diagnosis of Parkinson’s disease. In this paper we have studied various techniques that have been used in the detection, classification and diagnosis of Parkinson’s disease. The features from the extracted voice samples of patients are analyzed using various classification models. The major classification models that are commonly used are Random Tree (RT), Support Vector Machine (SVM) and Artificial Neural Network (ANN). This has great potential to increase the healthcare facilities in the remote areas where effective diagnosis is difficult.
Support Vector Machines, Artificial Neural Network, Parkinson’s disease.