Parkinson's Disease Diagnosis Using Voice Features and Effective Machine Learning Methods

Abstract

A neurodegenerative condition that largely affects the central nervous system is Parkinson's disease, and subtle changes in the early stages of this disease make accurate diagnosis challenging. 'Bradykinesia', or sluggish movements, are among the disease's typical symptoms. The disease's symptoms start to show up in middle age, and as people age, the severity of the condition worsens. A speech issue is one of the first indications of Parkinson's disease. In this work, it is suggested that employing supervised classification algorithms for the subjective disease categorization, such as support vector machines (SVM), artificial neural networks (ANN), Naive Bayes (NB), etc. could be successful. The proposed method is compared with previously used Parkinson's disease diagnosis methods and well-known classifiers. The experimental results show that ANN is better than other supervised algorithms with the highest accuracy. The proposed work provides equivalent and superior outcomes.