A Parkinson’s disease diagnosis approach for nonequilibrium gait data

Abstract

An important feature of Parkinson’s disease (PD) patients is dyskinesia, and gait signal analysis can provide a strong basis for the diagnosis and rehabilitation of Parkinson’s disease. Traditional machine learning methods are not suitable for directly classifying imbalanced data. In order to accurately distinguish healthy people from Parkinson’s disease patients, a cost-sensitive support vector machine (CS-SVM) method is designed in this paper, which is used to construct the model for classification of gait signals between Kinson’s disease patients and healthy individuals. Gait data for the entire subject was extracted from a real U-shaped electronic walkway. The extracted features are converted to a dimensionless form, the classification performance can be improved. The experimental results show that the prediction accuracy and F-measure obtained by the CS-SVM method are 94.16% and 87.08%, respectively.