Parkinson's disease patients classification based on the speech signals

Parkinson's disease is the second most frequent neurodegenerative disorder after Alzheimer's disease. There are numerous symptoms among the population suffering from the disease including tremor, slowed movement, impaired posture and balance, and rigid muscles, however dysphonia — changes in speech and articulation — is the most significant precursor. This is the reason why the article is focused on patients classification based on their speech signals. Algorithms C4.5, C5.0, RandomForest and CART were used to generate the decision trees in Rstudio interface. In addition, cut-off values of individual attributes were applied in order to classify the patients. The dataset in the article consists of 40 individuals' records, half of which were affected by Parkinson's disease. Each individual's coverage was represented by several records such as permanent vowels pronunciation, certain words, numbers and sentences. The objective was to determine the most accurate classification model (decision tree) using the individual types of speech signals. Cross-validation method was used for evaluation of models. The highest average model accuracy of 66.5% was obtained for data taken when individuals pronounced the numbers.