Descriptive and inferential analysis of features for Dysphonia and Dysarthria Parkinson's disease symptoms

Parkinson's disease is related to nervous system disorders in human beings. In this paper, the status of Parkinson's disease has been predicted based on the analysis of features of two Parkinson's disease symptoms: Dysphonia and Dysarthria. The Dysphonia symptom has been measured by analyzing the features extracted from the vocal sounds of a patient. The Dysarthria symptom of Parkinson's disease has been analyzed by the features extracted from the speech signals of a patient. Hence, two different prediction models have been built for deriving the status of Parkinson's disease. The derivation of these prediction systems is based on statistical and discriminant feature analysis. The statistical-based feature analysis is carried out using descriptive and inferential analysis. In contrast, the enteric, deviation, and correlation-based information between the features are utilized for descriptive analysis. The inferential analysis has been performed using hypothetical testing of the distribution of features. The descriptive and inferential feature analyses are used to derive effective features for the Parkinson's disease problem based on the Dysphonia and Dysarthria symptoms. In the discriminant feature analysis, the feature sets undergo deriving discriminant features. The best-selected features are used to build a prediction model for detecting Parkinson's disease problems in a patient. Extensive experimentation has been carried out using a Dysphonia dataset containing 195 samples. Each sample has 23 features. One dataset for Dysarthria symptoms contains 756 observations, with each observation having 754 features. The best prediction models that have been obtained correspond to these datasets, and 93.50% is obtained for Dysphonia and 87.63% for Dysarthria symptoms databases, respectively. The performance of the proposed system has been compared with some existing methods concerning each employed dataset, showing the superiority of the proposed approach.

Automated summary

This paper predicts the status of Parkinson's disease based on the analysis of two symptoms: Dysphonia and Dysarthria. Statistical and discriminant feature analysis are used to derive effective features and build prediction models, which outperform existing methods. The proposed approach achieved a prediction accuracy of 93.50% for Dysphonia and 87.63% for Dysarthria symptoms datasets.