UTILIZING PROCESSED RECORDS OF PATIENT´S SPEECH IN DETERMINING THE STAGE OF PARKINSON´S DISEASE

Abstract

The medical procedures for disease diagnostics are significantly demanding and time-consuming. Data mining methods can accelerate this process and assist doctors in making decisions in complex situations. In case of Parkinson ́s disease (PD), the diagnostics of the initial disease stage is the primary issue since the symptoms are not so unambiguous and easily observable. Therefore, this article is focused on determining the actual stage of PD based on the data recording signals of patient ́s speech using decision trees (C4.5, C5.0 and CART). Methods such as RandomForest, Bagging and Boosting were also employed to improve the existing classification models. Estimation of model accuracy was achieved by using k-fold cross-validation and validation with omission of one record (Leave-one-out). In addition, experiments were also performed to remove collinearity in data by computing the Variance inflation factor (VIF) in order to increase the accuracy of the models.