Features dimensionality reduction and multi-dimensional voice processing program to Parkinson disease discrimination

Parkinson's disease is a pathology that involves characteristic perturbations in patients' voices. This paper describes a proposed method that aims to diagnose persons with Parkinson (PWP) by analyzing on line their voices signals. First, Thresholds signals alterations are determined by the Multi-Dimensional Voice Program (MDVP). Principal Analysis (PCA) is exploited to select the main voice principal components that are significantly affected in a patient. The decision phase is realized by a Multinomial Bayes (MNB) Classifier that categorizes an analyzed voice in one of the two resulting classes: healthy or PWP. The prediction accuracy achieved reaching 98.8% is very promising.