A Machine Learning Approach to Detect Parkinson Disease Using Speech Signals

One of the leading neuro-degenerative disorders is Parkinson's disease (PD), which affects a large number of people worldwide. Diagnostics of PD are complex because a detailed clinical review of the patient's medical record is necessary. One of the characteristic and predictive indicators of this disease is voice. Almost all such patients experience at least some vocal degeneration; therefore voice data is an effective way to diagnose the disease. This paper presents a method for analyzing voice impairments by means of a system design in people who have PD and in people who are not afflicted with the disease. Various methodologies including Artificial Neural Networks, Logistic Regression, Decision Trees, SVM, and Random Forests were applied in this study. To test how well these algorithms can discriminate between PD and healthy subjects, the best accuracy predictor model can be determined out of this implemented model. With an accuracy of 88.81%, neural network ended up being the best algorithm among all of the algorithms used during this study.