Severity Classification of Parkinson’s Disease Based on Permutation-Variable Importance and Persistent Entropy

Abstract

Parkinson’s disease (PD) is a neurodegenerative disease that causes chronic and progressive motor dysfunction. As PD progresses, patients show different symptoms at different stages of the disease. The severity assessment is inefficient and subjective when it comes to artificial diagnosis. However, abnormal gait was contingent and the subject selection was limited. Therefore, few-shot learning based on small sample sets is critical to solving the problem of insufficient sample data in PD patients. Using datasets from PhysioNet, this paper presents a method based on permutation-variable importance (PVI) and persistent entropy of topological imprints, and uses support vector machine (SVM) as a classifier to achieve the severity classification of PD patients. The method includes the following steps: (1) Take the data as gait cycles, and calculate the gait characteristics of each cycle. (2) Use the random forest (RF) method to obtain the leading factors differentiating the gait of patients at different severity levels. (3) Use time-delay embedding to map the data into a topological space, and use the topological data analysis based on permutation homology to obtain the persistent entropy. (4) Use the Borderline-SMOTE (BSM) method to balance the sample data. (5) Use the SVM to classify the samples for the severity levels of PD. An accuracy of 98.08% was achieved by 10-fold cross-validation, so our method can be used as an effective means of computer-aided diagnosis of PD, and has important practical value.